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Monitoring and managing system status and performance

Red Hat Enterprise Linux 8

Optimizing system throughput, latency, and power consumption

Red Hat Customer Content Services

Abstract

This documentation collection provides instructions on how to monitor and optimize the throughput, latency, and power consumption of Red Hat Enterprise Linux 8 in different scenarios.

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Chapter 1. Overview of performance monitoring options

The following are some of the performance monitoring and configuration tools available in Red Hat Enterprise Linux 8:

  • Performance Co-Pilot (pcp) is used for monitoring, visualizing, storing, and analyzing system-level performance measurements. It allows the monitoring and management of real-time data, and logging and retrieval of historical data.
  • Red Hat Enterprise Linux 8 provides several tools that can be used from the command line to monitor a system outside run level 5. The following are the built-in command line tools:

    • top is provided by the procps-ng package. It gives a dynamic view of the processes in a running system. It displays a variety of information, including a system summary and a list of tasks currently being managed by the Linux kernel.
    • ps is provided by the procps-ng package. It captures a snapshot of a select group of active processes. By default, the examined group is limited to processes that are owned by the current user and associated with the terminal where the ps command is executed.
    • Virtual memory statistics (vmstat) is provided by the procps-ng package. It provides instant reports of your system’s processes, memory, paging, block input/output, interrupts, and CPU activity.
    • System activity reporter (sar) is provided by the sysstat package. It collects and reports information about system activity that has occurred so far on the current day.
  • perf uses hardware performance counters and kernel trace-points to track the impact of other commands and applications on a system.
  • bcc-tools is used for BPF Compiler Collection (BCC). It provides over 100 eBPF scripts that monitor kernel activities. For more information on each of this tool, see the man page describing how to use it and what functions it performs.
  • turbostat is provided by the kernel-tools package. It reports on processor topology, frequency, idle power-state statistics, temperature, and power usage on the Intel 64 processors.
  • iostat is provided by the sysstat package. It monitors and reports on system input/output device loading to help administrators make decisions about how to balance input/output load between physical disks.
  • irqbalance distributes hardware interrupts across processors to improve system performance.
  • ss prints statistical information about sockets, allowing administrators to assess device performance over time. Red Hat recommends using ss over netstat in Red Hat Enterprise Linux 8.
  • numastat is provided by the numactl package. By default, numastat displays per-node NUMA hit an miss system statistics from the kernel memory allocator. Optimal performance is indicated by high numa_hit values and low numa_miss values.
  • numad is an automatic NUMA affinity management daemon. It monitors NUMA topology and resource usage within a system that dynamically improves NUMA resource allocation, management, and therefore system performance.
  • SystemTap monitors and analyzes operating system activities, especially the kernel activities.
  • OProfile monitors the system wide performance. It uses the processor’s dedicated performance monitoring hardware to retrieve information about the kernel and system executable to determine the frequency of certain events. These events can be when the memory is referenced, the number of second-level cache requests, and the number of hardware requests received.
  • valgrind analyzes applications by running it on a synthetic CPU and instrumenting existing application code as it is executed. It then prints commentary that clearly identifies each process involved in application execution to a user-specified file, file descriptor, or network socket. It is also useful for finding memory leaks.
  • pqos is provided by the the intel-cmt-cat package. It monitors and controls CPU cache and memory bandwidth on recent Intel processors.

Additional resources

  • For more information, see the man pages of pcp, top, ps, vmstat, sar, perf, iostat, irqbalance, ss, numastat, numad, valgrind, and pqos.
  • For more information on oprofile and pcp, see the documentation in the /usr/share/doc/ directory.
  • For more information on the await value and what can cause its values to be high, see the Red Hat Knowledgebase article: What exactly is the meaning of value "await" reported by iostat?.

Chapter 2. Getting started with Tuned

As a system administrator, you can use the Tuned application to optimize the performance profile of your system for a variety of use cases.

2.1. The purpose of Tuned

Tuned is a service that monitors your system and optimizes the performance under certain workloads. The core of Tuned are profiles, which tune your system for different use cases.

Tuned is distributed with a number of predefined profiles for use cases such as:

  • High throughput
  • Low latency
  • Saving power

It is possible to modify the rules defined for each profile and customize how to tune a particular device. When you switch to another profile or deactivate Tuned, all changes made to the system settings by the previous profile revert back to their original state.

You can also configure Tuned to react to changes in device usage and adjusts settings to improve performance of active devices and reduce power consumption of inactive devices.

2.2. Tuned profiles

A detailed analysis of a system can be very time-consuming. Tuned provides a number of predefined profiles for typical use cases. You can also create, modify, and delete profiles.

The profiles provided with Tuned are divided into the following categories:

  • Power-saving profiles
  • Performance-boosting profiles

The performance-boosting profiles include profiles that focus on the following aspects:

  • Low latency for storage and network
  • High throughput for storage and network
  • Virtual machine performance
  • Virtualization host performance

The default profile

During the installation, the best profile for your system is selected automatically. Currently, the default profile is selected according to the following customizable rules:

EnvironmentDefault profileGoal

Compute nodes

throughput-performance

The best throughput performance

Virtual machines

virtual-guest

The best performance. If you are not interested in the best performance, you can change it to the balanced or powersave profile.

Other cases

balanced

Balanced performance and power consumption

Merged profiles

As an experimental feature, it is possible to select more profiles at once. Tuned will try to merge them during the load.

If there are conflicts, the settings from the last specified profile takes precedence.

Example 2.1. Low power consumption in a virtual guest

The following example optimizes the system to run in a virtual machine for the best performance and concurrently tunes it for low power consumption, while the low power consumption is the priority:

# tuned-adm profile virtual-guest powersave
Warning

Merging is done automatically without checking whether the resulting combination of parameters makes sense. Consequently, the feature might tune some parameters the opposite way, which might be counterproductive: for example, setting the disk for high throughput by using the throughput-performance profile and concurrently setting the disk spindown to the low value by the spindown-disk profile.

The location of profiles

Tuned stores profiles in the following directories:

/usr/lib/tuned/
Distribution-specific profiles are stored in the directory. Each profile has its own directory. The profile consists of the main configuration file called tuned.conf, and optionally other files, for example helper scripts.
/etc/tuned/
If you need to customize a profile, copy the profile directory into the directory, which is used for custom profiles. If there are two profiles of the same name, the custom profile located in /etc/tuned/ is used.

The syntax of profile configuration

The tuned.conf file can contain one [main] section and other sections for configuring plug-in instances. However, all sections are optional.

Lines starting with the hash sign (#) are comments.

Additional resources

  • The tuned.conf(5) man page.

2.3. Tuned profiles distributed with RHEL

The following is a list of profiles that are installed with Tuned on Red Hat Enterprise Linux.

Note

There might be more product-specific or third-party Tuned profiles available. Such profiles are usually provided by separate RPM packages.

balanced
The default power-saving profile. It is intended to be a compromise between performance and power consumption. It uses auto-scaling and auto-tuning whenever possible. The only drawback is the increased latency. In the current Tuned release, it enables the CPU, disk, audio, and video plugins, and activates the conservative CPU governor. The radeon_powersave option uses the dpm-balanced value if it is supported, otherwise it is set to auto.
powersave

A profile for maximum power saving performance. It can throttle the performance in order to minimize the actual power consumption. In the current Tuned release it enables USB autosuspend, WiFi power saving, and Aggressive Link Power Management (ALPM) power savings for SATA host adapters. It also schedules multi-core power savings for systems with a low wakeup rate and activates the ondemand governor. It enables AC97 audio power saving or, depending on your system, HDA-Intel power savings with a 10 seconds timeout. If your system contains a supported Radeon graphics card with enabled KMS, the profile configures it to automatic power saving. On ASUS Eee PCs, a dynamic Super Hybrid Engine is enabled.

Note

In certain cases, the balanced profile is more efficient compared to the powersave profile.

Consider there is a defined amount of work that needs to be done, for example a video file that needs to be transcoded. Your machine might consume less energy if the transcoding is done on the full power, because the task is finished quickly, the machine starts to idle, and it can automatically step-down to very efficient power save modes. On the other hand, if you transcode the file with a throttled machine, the machine consumes less power during the transcoding, but the process takes longer and the overall consumed energy can be higher.

That is why the balanced profile can be generally a better option.

throughput-performance
A server profile optimized for high throughput. It disables power savings mechanisms and enables sysctl settings that improve the throughput performance of the disk and network IO. CPU governor is set to performance.
latency-performance
A server profile optimized for low latency. It disables power savings mechanisms and enables sysctl settings that improve latency. CPU governor is set to performance and the CPU is locked to the low C states (by PM QoS).
network-latency
A profile for low latency network tuning. It is based on the latency-performance profile. It additionally disables transparent huge pages and NUMA balancing, and tunes several other network-related sysctl parameters.
network-throughput
A profile for throughput network tuning. It is based on the throughput-performance profile. It additionally increases kernel network buffers.
virtual-guest
A profile designed for virtual guests based on the throughput-performance profile that, among other tasks, decreases virtual memory swappiness and increases disk readahead values. It does not disable disk barriers.
virtual-host
A profile designed for virtual hosts based on the throughput-performance profile that, among other tasks, decreases virtual memory swappiness, increases disk readahead values, and enables a more aggressive value of dirty pages writeback.
oracle
A profile optimized for Oracle databases loads based on throughput-performance profile. It additionally disables transparent huge pages and modifies other performance-related kernel parameters. This profile is provided by the tuned-profiles-oracle package.
desktop
A profile optimized for desktops, based on the balanced profile. It additionally enables scheduler autogroups for better response of interactive applications.
cpu-partitioning

The cpu-partitioning profile partitions the system CPUs into isolated and housekeeping CPUs. To reduce jitter and interruptions on an isolated CPU, the profile clears the isolated CPU from user-space processes, movable kernel threads, interrupt handlers, and kernel timers.

A housekeeping CPU can run all services, shell processes, and kernel threads.

You can configure the cpu-partitioning profile in /etc/tuned/cpu-partitioning-variables.conf file. The configuration options are:

isolated_cores=cpu-list
Lists CPUs to isolate. The list of isolated CPUs is comma-separated or the user can specify the range. You can specify a range using a dash, such as 3-5. This option is mandatory. Any CPU missing from this list is automatically considered a housekeeping CPU.
no_balance_cores=cpu-list
Lists CPUs which are not considered by the kernel during system wide process load-balancing. This option is optional. This is usually the same list as isolated_cores.

For more information on cpu-partitioning, see the tuned-profiles-cpu-partitioning(7) man page.

Real-time profiles

Real-time profiles are intended for systems running the real-time kernel. Without a special kernel build, they do not configure the system to be real-time. On RHEL, the profiles are available from additional repositories.

The following real-time profiles are available:

realtime

Use on bare-metal real-time systems.

Provided by the tuned-profiles-realtime package, which is available from the RT or NFV repositories.

realtime-virtual-host

Use in a virtualization host configured for real-time.

Provided by the tuned-profiles-nfv-host package, which is available from the NFV repository.

realtime-virtual-guest

Use in a virtualization guest configured for real-time.

Provided by the tuned-profiles-nfv-guest package, which is available from the NFV repository.

2.4. Static and dynamic tuning in Tuned

This section explains the difference between the two categories of system tuning that Tuned applies: static and dynamic.

Static tuning
Mainly consists of the application of predefined sysctl and sysfs settings and one-shot activation of several configuration tools such as ethtool.
Dynamic tuning

Watches how various system components are used throughout the uptime of your system. Tuned adjusts system settings dynamically based on that monitoring information.

For example, the hard drive is used heavily during startup and login, but is barely used later when the user might mainly work with applications such as web browsers or email clients. Similarly, the CPU and network devices are used differently at different times. Tuned monitors the activity of these components and reacts to the changes in their use.

By default, dynamic tuning is disabled. To enable it, edit the /etc/tuned/tuned-main.conf file and change the dynamic_tuning option to 1. Tuned then periodically analyzes system statistics and uses them to update your system tuning settings. To configure the time interval in seconds between these updates, use the update_interval option.

Currently implemented dynamic tuning algorithms try to balance the performance and powersave, and are therefore disabled in the performance profiles. Dynamic tuning for individual plug-ins can be enabled or disabled in the Tuned profiles.

Example 2.2. Static and dynamic tuning on a workstation

On a typical office workstation, the Ethernet network interface is inactive most of the time. Only a few emails go in and out or some web pages might be loaded.

For those kinds of loads, the network interface does not have to run at full speed all the time, as it does by default. Tuned has a monitoring and tuning plug-in for network devices that can detect this low activity and then automatically lower the speed of that interface, typically resulting in a lower power usage.

If the activity on the interface increases for a longer period of time, for example because a DVD image is being downloaded or an email with a large attachment is opened, Tuned detects this and sets the interface speed to maximum to offer the best performance while the activity level is high.

This principle is used for other plug-ins for CPU and disks as well.

2.5. Tuned no-daemon mode

You can run Tuned in no-daemon mode, which does not require any resident memory. In this mode, Tuned applies the settings and exits.

By default, no-daemon mode is disabled because a lot of Tuned functionality is missing in this mode, including:

  • D-Bus support
  • Hot-plug support
  • Rollback support for settings

To enable no-daemon mode, include the following line in the /etc/tuned/tuned-main.conf file:

daemon = 0

2.6. Installing and enabling Tuned

This procedure installs and enables the Tuned application, installs Tuned profiles, and presets a default Tuned profile for your system.

Procedure

  1. Install the tuned package:

    # yum install tuned
  2. Enable and start the tuned service:

    # systemctl enable --now tuned
  3. Optionally, install Tuned profiles for real-time systems:

    # yum install tuned-profiles-realtime tuned-profiles-nfv
  4. Verify that a Tuned profile is active and applied:

    $ tuned-adm active
    
    Current active profile: balanced
    $ tuned-adm verify
    
    Verfication succeeded, current system settings match the preset profile.
    See tuned log file ('/var/log/tuned/tuned.log') for details.

2.7. Listing available Tuned profiles

This procedure lists all Tuned profiles that are currently available on your system.

Procedure

  • To list all available Tuned profiles on your system, use:

    $ tuned-adm list
    
    Available profiles:
    - balanced               - General non-specialized tuned profile
    - desktop                - Optimize for the desktop use-case
    - latency-performance    - Optimize for deterministic performance at the cost of increased power consumption
    - network-latency        - Optimize for deterministic performance at the cost of increased power consumption, focused on low latency network performance
    - network-throughput     - Optimize for streaming network throughput, generally only necessary on older CPUs or 40G+ networks
    - powersave              - Optimize for low power consumption
    - throughput-performance - Broadly applicable tuning that provides excellent performance across a variety of common server workloads
    - virtual-guest          - Optimize for running inside a virtual guest
    - virtual-host           - Optimize for running KVM guests
    Current active profile: balanced
  • To display only the currently active profile, use:

    $ tuned-adm active
    
    Current active profile: balanced

Additional resources

  • The tuned-adm(8) man page.

2.8. Setting a Tuned profile

This procedure activates a selected Tuned profile on your system.

Prerequisites

Procedure

  1. Optionally, you can let Tuned recommend the most suitable profile for your system:

    # tuned-adm recommend
    
    balanced
  2. Activate a profile:

    # tuned-adm profile selected-profile

    Alternatively, you can activate a combination of multiple profiles:

    # tuned-adm profile profile1 profile2

    Example 2.3. A virtual machine optimized for low power consumption

    The following example optimizes the system to run in a virtual machine with the best performance and concurrently tunes it for low power consumption, while the low power consumption is the priority:

    # tuned-adm profile virtual-guest powersave
  3. View the current active Tuned profile on your system:

    # tuned-adm active
    
    Current active profile: selected-profile
  4. Reboot the system:

    # reboot

Verification steps

  • Verify that the Tuned profile is active and applied:

    $ tuned-adm verify
    
    Verfication succeeded, current system settings match the preset profile.
    See tuned log file ('/var/log/tuned/tuned.log') for details.

Additional resources

  • The tuned-adm(8) man page

2.9. Disabling Tuned

This procedure disables Tuned and resets all affected system settings to their original state before Tuned modified them.

Procedure

  • To disable all tunings temporarily:

    # tuned-adm off

    The tunings are applied again after the tuned service restarts.

  • Alternatively, to stop and disable the tuned service permanently:

    # systemctl disable --now tuned

Additional resources

  • The tuned-adm(8) man page.

Chapter 3. Customizing Tuned profiles

You can create or modify Tuned profiles to optimize system performance for your intended use case.

Prerequisites

3.1. Tuned profiles

A detailed analysis of a system can be very time-consuming. Tuned provides a number of predefined profiles for typical use cases. You can also create, modify, and delete profiles.

The profiles provided with Tuned are divided into the following categories:

  • Power-saving profiles
  • Performance-boosting profiles

The performance-boosting profiles include profiles that focus on the following aspects:

  • Low latency for storage and network
  • High throughput for storage and network
  • Virtual machine performance
  • Virtualization host performance

The default profile

During the installation, the best profile for your system is selected automatically. Currently, the default profile is selected according to the following customizable rules:

EnvironmentDefault profileGoal

Compute nodes

throughput-performance

The best throughput performance

Virtual machines

virtual-guest

The best performance. If you are not interested in the best performance, you can change it to the balanced or powersave profile.

Other cases

balanced

Balanced performance and power consumption

Merged profiles

As an experimental feature, it is possible to select more profiles at once. Tuned will try to merge them during the load.

If there are conflicts, the settings from the last specified profile takes precedence.

Example 3.1. Low power consumption in a virtual guest

The following example optimizes the system to run in a virtual machine for the best performance and concurrently tunes it for low power consumption, while the low power consumption is the priority:

# tuned-adm profile virtual-guest powersave
Warning

Merging is done automatically without checking whether the resulting combination of parameters makes sense. Consequently, the feature might tune some parameters the opposite way, which might be counterproductive: for example, setting the disk for high throughput by using the throughput-performance profile and concurrently setting the disk spindown to the low value by the spindown-disk profile.

The location of profiles

Tuned stores profiles in the following directories:

/usr/lib/tuned/
Distribution-specific profiles are stored in the directory. Each profile has its own directory. The profile consists of the main configuration file called tuned.conf, and optionally other files, for example helper scripts.
/etc/tuned/
If you need to customize a profile, copy the profile directory into the directory, which is used for custom profiles. If there are two profiles of the same name, the custom profile located in /etc/tuned/ is used.

The syntax of profile configuration

The tuned.conf file can contain one [main] section and other sections for configuring plug-in instances. However, all sections are optional.

Lines starting with the hash sign (#) are comments.

Additional resources

  • The tuned.conf(5) man page.

3.2. Inheritance between Tuned profiles

Tuned profiles can be based on other profiles and modify only certain aspects of their parent profile.

The [main] section of Tuned profiles recognizes the include option:

[main]
include=parent

All settings from the parent profile are loaded in this child profile. In the following sections, the child profile can override certain settings inherited from the parent profile or add new settings not present in the parent profile.

You can create your own child profile in the /etc/tuned/ directory based on a pre-installed profile in /usr/lib/tuned/ with only some parameters adjusted.

If the parent profile is updated, such as after a Tuned upgrade, the changes are reflected in the child profile.

Example 3.2. A power-saving profile based on balanced

The following is an example of a custom profile that extends the balanced profile and sets Aggressive Link Power Management (ALPM) for all devices to the maximum powersaving.

[main]
include=balanced

[scsi_host]
alpm=min_power

Additional resources

  • The tuned.conf(5) man page

3.3. Static and dynamic tuning in Tuned

This section explains the difference between the two categories of system tuning that Tuned applies: static and dynamic.

Static tuning
Mainly consists of the application of predefined sysctl and sysfs settings and one-shot activation of several configuration tools such as ethtool.
Dynamic tuning

Watches how various system components are used throughout the uptime of your system. Tuned adjusts system settings dynamically based on that monitoring information.

For example, the hard drive is used heavily during startup and login, but is barely used later when the user might mainly work with applications such as web browsers or email clients. Similarly, the CPU and network devices are used differently at different times. Tuned monitors the activity of these components and reacts to the changes in their use.

By default, dynamic tuning is disabled. To enable it, edit the /etc/tuned/tuned-main.conf file and change the dynamic_tuning option to 1. Tuned then periodically analyzes system statistics and uses them to update your system tuning settings. To configure the time interval in seconds between these updates, use the update_interval option.

Currently implemented dynamic tuning algorithms try to balance the performance and powersave, and are therefore disabled in the performance profiles. Dynamic tuning for individual plug-ins can be enabled or disabled in the Tuned profiles.

Example 3.3. Static and dynamic tuning on a workstation

On a typical office workstation, the Ethernet network interface is inactive most of the time. Only a few emails go in and out or some web pages might be loaded.

For those kinds of loads, the network interface does not have to run at full speed all the time, as it does by default. Tuned has a monitoring and tuning plug-in for network devices that can detect this low activity and then automatically lower the speed of that interface, typically resulting in a lower power usage.

If the activity on the interface increases for a longer period of time, for example because a DVD image is being downloaded or an email with a large attachment is opened, Tuned detects this and sets the interface speed to maximum to offer the best performance while the activity level is high.

This principle is used for other plug-ins for CPU and disks as well.

3.4. Tuned plug-ins

Plug-ins are modules in Tuned profiles that Tuned uses to monitor or optimize different devices on the system.

Tuned uses two types of plug-ins:

  • monitoring plug-ins
  • tuning plug-ins

Monitoring plug-ins

Monitoring plug-ins are used to get information from a running system. The output of the monitoring plug-ins can be used by tuning plug-ins for dynamic tuning.

Monitoring plug-ins are automatically instantiated whenever their metrics are needed by any of the enabled tuning plug-ins. If two tuning plug-ins require the same data, only one instance of the monitoring plug-in is created and the data is shared.

Tuning plug-ins

Each tuning plug-in tunes an individual subsystem and takes several parameters that are populated from the tuned profiles. Each subsystem can have multiple devices, such as multiple CPUs or network cards, that are handled by individual instances of the tuning plug-ins. Specific settings for individual devices are also supported.

Syntax for plug-ins in Tuned profiles

Sections describing plug-in instances are formatted in the following way:

[NAME]
type=TYPE
devices=DEVICES
NAME
is the name of the plug-in instance as it is used in the logs. It can be an arbitrary string.
TYPE
is the type of the tuning plug-in.
DEVICES

is the list of devices that this plug-in instance handles.

The devices line can contain a list, a wildcard (*), and negation (!). If there is no devices line, all devices present or later attached on the system of the TYPE are handled by the plug-in instance. This is same as using the devices=* option.

Example 3.4. Matching block devices with a plug-in

The following example matches all block devices starting with sd, such as sda or sdb, and does not disable barriers on them:

[data_disk]
type=disk
devices=sd*
disable_barriers=false

The following example matches all block devices except sda1 and sda2:

[data_disk]
type=disk
devices=!sda1, !sda2
disable_barriers=false

If no instance of a plug-in is specified, the plug-in is not enabled.

If the plug-in supports more options, they can be also specified in the plug-in section. If the option is not specified and it was not previously specified in the included plug-in, the default value is used.

Short plug-in syntax

If you do not need custom names for the plug-in instance and there is only one definition of the instance in your configuration file, Tuned supports the following short syntax:

[TYPE]
devices=DEVICES

In this case, it is possible to omit the type line. The instance is then referred to with a name, same as the type. The previous example could be then rewritten into:

Example 3.5. Matching block devices using the short syntax

[disk]
devices=sdb*
disable_barriers=false

Conflicting plug-in definitions in a profile

If the same section is specified more than once using the include option, the settings are merged. If they cannot be merged due to a conflict, the last conflicting definition overrides the previous settings. If you do not know what was previously defined, you can use the replace Boolean option and set it to true. This causes all the previous definitions with the same name to be overwritten and the merge does not happen.

You can also disable the plug-in by specifying the enabled=false option. This has the same effect as if the instance was never defined. Disabling the plug-in is useful if you are redefining the previous definition from the include option and do not want the plug-in to be active in your custom profile.

Functionality not implemented in any plug-in

Tuned includes the ability to run any shell command as part of enabling or disabling a tuning profile. This enables you to extend Tuned profiles with functionality that has not been integrated into Tuned yet.

You can specify arbitrary shell commands using the script plug-in.

Additional resources

  • The tuned.conf(5) man page

3.5. Available Tuned plug-ins

This section lists all monitoring and tuning plug-ins currently available in Tuned.

Monitoring plug-ins

Currently, the following monitoring plug-ins are implemented:

disk
Gets disk load (number of IO operations) per device and measurement interval.
net
Gets network load (number of transferred packets) per network card and measurement interval.
load
Gets CPU load per CPU and measurement interval.

Tuning plug-ins

Currently, the following tuning plug-ins are implemented. Only some of these plug-ins implement dynamic tuning. Options supported by plug-ins are also listed:

cpu

Sets the CPU governor to the value specified by the governor option and dynamically changes the Power Management Quality of Service (PM QoS) CPU Direct Memory Access (DMA) latency according to the CPU load.

If the CPU load is lower than the value specified by the load_threshold option, the latency is set to the value specified by the latency_high option, otherwise it is set to the value specified by latency_low.

You can also force the latency to a specific value and prevent it from dynamically changing further. To do so, set the force_latency option to the required latency value.

eeepc_she

Dynamically sets the front-side bus (FSB) speed according to the CPU load.

This feature can be found on some netbooks and is also known as the ASUS Super Hybrid Engine (SHE).

If the CPU load is lower or equal to the value specified by the load_threshold_powersave option, the plug-in sets the FSB speed to the value specified by the she_powersave option. If the CPU load is higher or equal to the value specified by the load_threshold_normal option, it sets the FSB speed to the value specified by the she_normal option.

Static tuning is not supported and the plug-in is transparently disabled if Tuned does not detect the hardware support for this feature.

net
Configures the Wake-on-LAN functionality to the values specified by the wake_on_lan option. It uses the same syntax as the ethtool utility. It also dynamically changes the interface speed according to the interface utilization.
sysctl

Sets various sysctl settings specified by the plug-in options.

The syntax is name=value, where name is the same as the name provided by the sysctl utility.

Use the sysctl plug-in if you need to change system settings that are not covered by other plug-ins available in Tuned. If the settings are covered by some specific plug-ins, prefer these plug-ins.

usb

Sets autosuspend timeout of USB devices to the value specified by the autosuspend parameter.

The value 0 means that autosuspend is disabled.

vm
Enables or disables transparent huge pages depending on the Boolean value of the transparent_hugepages option.
audio

Sets the autosuspend timeout for audio codecs to the value specified by the timeout option.

Currently, the snd_hda_intel and snd_ac97_codec codecs are supported. The value 0 means that the autosuspend is disabled. You can also enforce the controller reset by setting the Boolean option reset_controller to true.

disk

Sets the disk elevator to the value specified by the elevator option.

It also sets:

  • APM to the value specified by the apm option
  • Scheduler quantum to the value specified by the scheduler_quantum option
  • Disk spindown timeout to the value specified by the spindown option
  • Disk readahead to the value specified by the readahead parameter
  • The current disk readahead to a value multiplied by the constant specified by the readahead_multiply option

In addition, this plug-in dynamically changes the advanced power management and spindown timeout setting for the drive according to the current drive utilization. The dynamic tuning can be controlled by the Boolean option dynamic and is enabled by default.

scsi_host

Tunes options for SCSI hosts.

It sets Aggressive Link Power Management (ALPM) to the value specified by the alpm option.

mounts
Enables or disables barriers for mounts according to the Boolean value of the disable_barriers option.
script

Executes an external script or binary when the profile is loaded or unloaded. You can choose an arbitrary executable.

Important

The script plug-in is provided mainly for compatibility with earlier releases. Prefer other Tuned plug-ins if they cover the required functionality.

Tuned calls the executable with one of the following arguments:

  • start when loading the profile
  • stop when unloading the profile

You need to correctly implement the stop action in your executable and revert all settings that you changed during the start action. Otherwise, the roll-back step after changing your Tuned profile will not work.

Bash scripts can import the /usr/lib/tuned/functions Bash library and use the functions defined there. Use these functions only for functionality that is not natively provided by Tuned. If a function name starts with an underscore, such as _wifi_set_power_level, consider the function private and do not use it in your scripts, because it might change in the future.

Specify the path to the executable using the script parameter in the plug-in configuration.

Example 3.6. Running a Bash script from a profile

To run a Bash script named script.sh that is located in the profile directory, use:

[script]
script=${i:PROFILE_DIR}/script.sh
sysfs

Sets various sysfs settings specified by the plug-in options.

The syntax is name=value, where name is the sysfs path to use.

Use this plugin in case you need to change some settings that are not covered by other plug-ins. Prefer specific plug-ins if they cover the required settings.

video

Sets various powersave levels on video cards. Currently, only the Radeon cards are supported.

The powersave level can be specified by using the radeon_powersave option. Supported values are:

  • default
  • auto
  • low
  • mid
  • high
  • dynpm
  • dpm-battery
  • dpm-balanced
  • dpm-perfomance

For details, see www.x.org. Note that this plug-in is experimental and the option might change in future releases.

bootloader

Adds options to the kernel command line. This plug-in supports only the GRUB 2 boot loader.

Customized non-standard location of the GRUB 2 configuration file can be specified by the grub2_cfg_file option.

The kernel options are added to the current GRUB configuration and its templates. The system needs to be rebooted for the kernel options to take effect.

Switching to another profile or manually stopping the tuned service removes the additional options. If you shut down or reboot the system, the kernel options persist in the grub.cfg file.

The kernel options can be specified by the following syntax:

cmdline=arg1 arg2 ... argN

Example 3.7. Modifying the kernel command line

For example, to add the quiet kernel option to a Tuned profile, include the following lines in the tuned.conf file:

[bootloader]
cmdline=quiet

The following is an example of a custom profile that adds the isolcpus=2 option to the kernel command line:

[bootloader]
cmdline=isolcpus=2

3.6. Variables and built-in functions in Tuned profiles

Variables and built-in functions expand at run time when a Tuned profile is activated.

Using Tuned variables reduces the amount of necessary typing in Tuned profiles. You can also:

  • Use various built-in functions together with Tuned variables
  • Create custom functions in Python and add them to Tuned in the form of plug-ins

Variables

There are no predefined variables in Tuned profiles. You can define your own variables by creating the [variables] section in a profile and using the following syntax:

[variables]

variable_name=value

To expand the value of a variable in a profile, use the following syntax:

${variable_name}

Example 3.8. Isolating CPU cores using variables

In the following example, the ${isolated_cores} variable expands to 1,2; hence the kernel boots with the isolcpus=1,2 option:

[variables]
isolated_cores=1,2

[bootloader]
cmdline=isolcpus=${isolated_cores}

The variables can be specified in a separate file. For example, you can add the following lines to tuned.conf:

[variables]
include=/etc/tuned/my-variables.conf

[bootloader]
cmdline=isolcpus=${isolated_cores}

If you add the isolated_cores=1,2 option to the /etc/tuned/my-variables.conf file, the kernel boots with the isolcpus=1,2 option.

Functions

To call a function, use the following syntax:

${f:function_name:argument_1:argument_2}

To expand the directory path where the profile and the tuned.conf file are located, use the PROFILE_DIR function, which requires special syntax:

${i:PROFILE_DIR}

Example 3.9. Isolating CPU cores using variables and built-in functions

In the following example, the ${non_isolated_cores} variable expands to 0,3-5, and the cpulist_invert built-in function is called with the 0,3-5 argument:

[variables]
non_isolated_cores=0,3-5

[bootloader]
cmdline=isolcpus=${f:cpulist_invert:${non_isolated_cores}}

The cpulist_invert function inverts the list of CPUs. For a 6-CPU machine, the inversion is 1,2, and the kernel boots with the isolcpus=1,2 command-line option.

Additional resources

  • The tuned.conf(5) man page

3.7. Built-in functions available in Tuned profiles

The following built-in functions are available in all Tuned profiles:

PROFILE_DIR
Returns the directory path where the profile and the tuned.conf file are located.
exec
Executes a process and returns its output.
assertion
Compares two arguments. If they do not match, the function logs text from the first argument and aborts profile loading.
assertion_non_equal
Compares two arguments. If they match, the function logs text from the first argument and aborts profile loading.
kb2s
Converts kilobytes to disk sectors.
s2kb
Converts disk sectors to kilobytes.
strip
Creates a string from all passed arguments and deletes both leading and trailing white space.
virt_check

Checks whether Tuned is running inside a virtual machine (VM) or on bare metal:

  • Inside a VM, the function returns the first argument.
  • On bare metal, the function returns the second argument, even in case of an error.
cpulist_invert
Inverts a list of CPUs to make its complement. For example, on a system with 4 CPUs, numbered from 0 to 3, the inversion of the list 0,2,3 is 1.
cpulist2hex
Converts a CPU list to a hexadecimal CPU mask.
cpulist2hex_invert
Converts a CPU list to a hexadecimal CPU mask and inverts it.
hex2cpulist
Converts a hexadecimal CPU mask to a CPU list.
cpulist_online
Checks whether the CPUs from the list are online. Returns the list containing only online CPUs.
cpulist_present
Checks whether the CPUs from the list are present. Returns the list containing only present CPUs.
cpulist_unpack
Unpacks a CPU list in the form of 1-3,4 to 1,2,3,4.
cpulist_pack
Packs a CPU list in the form of 1,2,3,5 to 1-3,5.

3.8. Creating new Tuned profiles

This procedure creates a new Tuned profile with custom performance rules.

Prerequisites

Procedure

  1. In the /etc/tuned/ directory, create a new directory named the same as the profile that you want to create:

    # mkdir /etc/tuned/my-profile
  2. In the new directory, create a file named tuned.conf. Add a [main] section and plug-in definitions in it, according to your requirements.

    For example, see the configuration of the balanced profile:

    [main]
    summary=General non-specialized tuned profile
    
    [cpu]
    governor=conservative
    energy_perf_bias=normal
    
    [audio]
    timeout=10
    
    [video]
    radeon_powersave=dpm-balanced, auto
    
    [scsi_host]
    alpm=medium_power
  3. To activate the profile, use:

    # tuned-adm profile my-profile
  4. Verify that the Tuned profile is active and the system settings are applied:

    $ tuned-adm active
    
    Current active profile: my-profile
    $ tuned-adm verify
    
    Verfication succeeded, current system settings match the preset profile.
    See tuned log file ('/var/log/tuned/tuned.log') for details.

Additional resources

  • The tuned.conf(5) man page

3.9. Modifying existing Tuned profiles

This procedure creates a modified child profile based on an existing Tuned profile.

Prerequisites

Procedure

  1. In the /etc/tuned/ directory, create a new directory named the same as the profile that you want to create:

    # mkdir /etc/tuned/modified-profile
  2. In the new directory, create a file named tuned.conf, and set the [main] section as follows:

    [main]
    include=parent-profile

    Replace parent-profile with the name of the profile you are modifying.

  3. Include your profile modifications.

    Example 3.10. Lowering swappiness in the throughput-performance profile

    To use the settings from the throughput-performance profile and change the value of vm.swappiness to 5, instead of the default 10, use:

    [main]
    include=throughput-performance
    
    [sysctl]
    vm.swappiness=5
  4. To activate the profile, use:

    # tuned-adm profile modified-profile
  5. Verify that the Tuned profile is active and the system settings are applied:

    $ tuned-adm active
    
    Current active profile: my-profile
    $ tuned-adm verify
    
    Verfication succeeded, current system settings match the preset profile.
    See tuned log file ('/var/log/tuned/tuned.log') for details.

Additional resources

  • The tuned.conf(5) man page

3.10. Setting the disk scheduler using Tuned

This procedure creates and enables a Tuned profile that sets a given disk scheduler for selected block devices. The setting persists across system reboots.

In the following commands and configuration, replace:

  • device with the name of the block device, for example sdf
  • selected-scheduler with the disk scheduler that you want to set for the device, for example bfq

Prerequisites

Procedure

  1. Optional: Select an existing Tuned profile on which your profile will be based. For a list of available profiles, see Section 2.3, “Tuned profiles distributed with RHEL”.

    To see which profile is currently active, use:

    $ tuned-adm active
  2. Create a new directory to hold your Tuned profile:

    # mkdir /etc/tuned/my-profile
  3. Find the World Wide Name (WWN) identifier of the selected block device:

    $ udevadm info --query=property --name=/dev/device | grep WWN=
    
    ID_WWN=0x5002538d00000000
  4. Create the /etc/tuned/my-profile/tuned.conf configuration file. In the file, set the following options:

    • Optional: Include an existing profile:

      [main]
      include=existing-profile
    • Set the selected disk scheduler for the device that matches the WWN identifier:

      [disk]
      devices_udev_regex=ID_WWN=0x5002538d00000000
      elevator=selected-scheduler

      To match multiple devices in the devices_udev_regex option, enclose the identifiers in parentheses and separate them with vertical bars:

      devices_udev_regex=(ID_WWN=0x5002538d00000000)|(ID_WWN=0x1234567800000000)
  5. Enable your profile:

    # tuned-adm profile my-profile
  6. Verify that the Tuned profile is active and applied:

    $ tuned-adm active
    
    Current active profile: my-profile
    $ tuned-adm verify
    
    Verification succeeded, current system settings match the preset profile.
    See tuned log file ('/var/log/tuned/tuned.log') for details.

Additional resources

Chapter 4. Monitoring performance with Performance Co-Pilot

As a system administrator, you can monitor the system’s performance using the Performance Co-Pilot (PCP) application in Red Hat Enterprise Linux 8.

4.1. Overview of PCP

PCP is a suite of tools, services, and libraries for monitoring, visualizing, storing, and analyzing system-level performance measurements.

Features of PCP:

  • Light-weight distributed architecture, which is useful during the centralized analysis of complex systems.
  • It allows the monitoring and management of real-time data.
  • It allows logging and retrieval of historical data.

You can add performance metrics using Python, Perl, C++, and C interfaces. Analysis tools can use the Python, C++, C client APIs directly, and rich web applications can explore all available performance data using a JSON interface.

You can analyze data patterns by comparing live results with archived data.

PCP has the following components:

  • The Performance Metric Collector Daemon (pmcd) collects performance data from the installed Performance Metric Domain Agents (pmda). PMDAs can be individually loaded or unloaded on the system and are controlled by the PMCD on the same host.
  • Various client tools, such as pminfo or pmstat, can retrieve, display, archive, and process this data on the same host or over the network.
  • The pcp package provides the command-line tools and underlying functionality.
  • The pcp-gui package provides the graphical application. Install the pcp-gui package by executing the yum install pcp-gui command. For more information, see Section 4.6, “Visually tracing PCP log archives with the PCP Charts application”.

Additional resources

4.2. Installing and enabling PCP

To begin using PCP, install all the required packages and enable the PCP monitoring services.

Procedure

  1. Install the PCP package:

    # yum install pcp
  2. Enable and start the pmcd service on the host machine:

    # systemctl enable pmcd
    
    # systemctl start pmcd
  3. Verify that the pmcd process is running on the host and the XFS PMDA is listed as enabled in the configuration:

    # pcp
    
    Performance Co-Pilot configuration on workstation:
    
    platform: Linux workstation 4.18.0-80.el8.x86_64 #1 SMP Wed Mar 13 12:02:46 UTC 2019 x86_64
    hardware: 12 cpus, 2 disks, 1 node, 36023MB RAM
    timezone: CEST-2
    services: pmcd
    pmcd: Version 4.3.0-1, 8 agents
    pmda: root pmcd proc xfs linux mmv kvm jbd2

Additional resources

4.3. Deploying a minimal PCP setup

The minimal PCP setup collects performance statistics on Red Hat Enterprise Linux. The setup involves adding the minimum number of packages on a production system needed to gather data for further analysis. You can analyze the resulting tar.gz file and the archive of the pmlogger output using various PCP tools and compare them with other sources of performance information.

Prerequisites

Procedure

  1. Update the pmlogger configuration:

    # pmlogconf -r /var/lib/pcp/config/pmlogger/config.default
  2. Start the pmcd and pmlogger services:

    # systemctl start pmcd.service
    
    # systemctl start pmlogger.service
  3. Execute the required operations to record the performance data.
  4. Stop the pmcd and pmlogger services:

    # systemctl stop pmcd.service
    
    # systemctl stop pmlogger.service
  5. Save the output and save it to a tar.gz file named based on the host name and the current date and time:

    # cd /var/log/pcp/pmlogger/
    
    # tar -czf $(hostname).$(date +%F-%Hh%M).pcp.tar.gz $(hostname)

    Extract this file and analyze the data using PCP tools.

4.4. Logging performance data with pmlogger

With the PCP tool you can log the performance metric values and replay them later. This allows you to perform a retrospective performance analysis.

Using the pmlogger tool, you can:

  • Create the archived logs of selected metrics on the system
  • Specify which metrics are recorded on the system and how often

4.4.1. Modifying the pmlogger configuration file with pmlogconf

When the pmlogger service is running, PCP logs a default set of metrics on the host. Use the pmlogconf utility to check the default configuration. If the pmlogger configuration file does not exist, pmlogconf creates it with a default metric values.

Prerequisites

Procedure

  1. Create or modify the pmlogger configuration file:

    # pmlogconf -r /var/lib/pcp/config/pmlogger/config.default
  2. Follow pmlogconf prompts to enable or disable groups of related performance metrics and to control the logging interval for each enabled group.

4.4.2. Editing the pmlogger configuration file manually

To create a tailored logging configuration with specific metrics and given intervals, edit the pmlogger configuration file manually.

In manual configuration, you can:

  • Record metrics which are not listed in the automatic configuration.
  • Choose custom logging frequencies.
  • Add PMDA with the application metrics.

The default pmlogger configuration file is /var/lib/pcp/config/pmlogger/config.default. The configuration file specifies which metrics are logged by the primary logging instance.

Prerequisites

Procedure

  • Open and edit the /var/lib/pcp/config/pmlogger/config.default file to add specific metrics:

    # It is safe to make additions from here on ...
    #
    
    log mandatory on every 5 seconds {
        xfs.write
        xfs.write_bytes
        xfs.read
        xfs.read_bytes
    }
    
    log mandatory on every 10 seconds {
        xfs.allocs
        xfs.block_map
        xfs.transactions
        xfs.log
    
    }
    
    [access]
    disallow * : all;
    allow localhost : enquire;

4.4.3. Enabling the pmlogger service

The pmlogger service must be started and enabled to log the metric values on the local machine.

Prerequisites

Procedure

  1. Start and enable the pmlogger service:

    # systemctl start pmlogger
    
    # systemctl enable pmlogger
  2. Verify that the pmlogger is enabled:

    # pcp
    
    Performance Co-Pilot configuration on workstation:
    
    platform: Linux workstation 4.18.0-80.el8.x86_64 #1 SMP Wed Mar 13 12:02:46 UTC 2019 x86_64
    hardware: 12 cpus, 2 disks, 1 node, 36023MB RAM
    timezone: CEST-2
    services: pmcd
    pmcd: Version 4.3.0-1, 8 agents, 1 client
    pmda: root pmcd proc xfs linux mmv kvm jbd2
    pmlogger: primary logger: /var/log/pcp/pmlogger/workstation/20190827.15.54

Additional resources

4.4.4. Setting up a client system for metrics collection

This procedure describes how to set up a client system so that a central server can collect metrics from clients running PCP.

Prerequisites

Procedure

  1. Install the pcp-system-tools package:

    # yum install pcp-system-tools
  2. Configure an IP address for pmcd:

    # echo "-i 192.168.4.62" >>/etc/pcp/pmcd/pmcd.options

    Replace 192.168.4.62 with the IP address, the client should listen on.

    By default, pmcd is listening on the localhost.

  3. Configure the firewall to add the public zone permanently:

    # firewall-cmd --permanent --zone=public --add-port=44321/tcp
    success
    
    # firewall-cmd --reload
    success
  4. Set an SELinux boolean:

    # setsebool -P pcp_bind_all_unreserved_ports on
  5. Enable the pmcd and pmlogger services:

    # systemctl enable pmcd pmlogger
    # systemctl restart pmcd pmlogger

Verification steps

  • Verify if the pmcd is correctly listening on the configured IP address:

    # ss -tlp | grep 44321
    LISTEN   0   5     127.0.0.1:44321   0.0.0.0:*   users:(("pmcd",pid=151595,fd=6))
    LISTEN   0   5  192.168.4.62:44321   0.0.0.0:*   users:(("pmcd",pid=151595,fd=0))
    LISTEN   0   5         [::1]:44321      [::]:*   users:(("pmcd",pid=151595,fd=7))

Additional resources

4.4.5. Setting up a central server to collect data

This procedure describes how to create a central server to collect metrics from clients running PCP.

Prerequisites

Procedure

  1. Install the pcp-system-tools package:

    # yum install pcp-system-tools
  2. Add clients for monitoring:

    # echo "192.168.4.13 n n PCP_LOG_DIR/pmlogger/rhel7u4a -r -T24h10m \
    -c config.remote"  >> /etc/pcp/pmlogger/control.d/remote
    
    # echo "192.168.4.14 n n PCP_LOG_DIR/pmlogger/rhel6u10a -r -T24h10m \
     -c config.remote" >> /etc/pcp/pmlogger/control.d/remote
    
    # echo "192.168.4.62 n n PCP_LOG_DIR/pmlogger/rhel8u1a -r -T24h10m \
    -c config.remote" >> /etc/pcp/pmlogger/control.d/remote

    Replace 192.168.4.13, 192.168.4.14, and 192.168.4.62 with the client IP addresses.

  3. Enable the pmcd and pmlogger services:

    # systemctl enable pmcd pmlogger
    # systemctl restart pmcd pmlogger

Verification steps

  • Ensure that you can access the latest archive file from each directory:

    # for i in /var/log/pcp/pmlogger/rhel*/*.0; do pmdumplog -L $i; done
    Log Label (Log Format Version 2)
    Performance metrics from host rhel6u10a.local
      commencing Mon Nov 25 21:55:04.851 2019
      ending     Mon Nov 25 22:06:04.874 2019
    Archive timezone: JST-9
    PID for pmlogger: 24002
    Log Label (Log Format Version 2)
    Performance metrics from host rhel7u4a
      commencing Tue Nov 26 06:49:24.954 2019
      ending     Tue Nov 26 07:06:24.979 2019
    Archive timezone: CET-1
    PID for pmlogger: 10941
    [..]

    The archive files from the /var/log/pcp/pmlogger/ directory can be used for further analysis and graphing.

Additional resources

4.4.6. Replaying the PCP log archives with pmdumptext

After recording the metric data, you can replay the PCP log archives. To export the logs to text files and import them into spreadsheets, use PCP utilities such as pmdumptext, pmrep, or pmlogsummary.

Using the pmdumptext tool, you can:

  • View the log files
  • Parse the selected PCP log archive and export the values into an ASCII table
  • Extract the entire archive log or only select metric values from the log by specifying individual metrics on the command line

Prerequisites

Procedure

  • Display the data on the metric:

    $ pmdumptext -t 5seconds -H -a 20170605 xfs.perdev.log.writes
    
    Time local::xfs.perdev.log.writes["/dev/mapper/fedora-home"] local::xfs.perdev.log.writes["/dev/mapper/fedora-root"]
    ? 0.000 0.000
    none count / second count / second
    Mon Jun 5 12:28:45 ? ?
    Mon Jun 5 12:28:50 0.000 0.000
    Mon Jun 5 12:28:55 0.200 0.200
    Mon Jun 5 12:29:00 6.800 1.000

    The mentioned example displays the data on the xfs.perdev.log metric collected in an archive at a 5 second interval and display all the headers.

4.5. Monitoring postfix with pmda-postfix

This procedure describes how to monitor performance metrics of the postfix mail server with pmda-postfix. It helps to check how many emails are received per second.

Prerequisites

Procedure

  1. Install the following packages:

    1. Install the pcp-system-tools:

      # yum install pcp-system-tools
    2. Install the pmda-postfix package to monitor postfix:

      # yum install pcp-pmda-postfix postfix
    3. Install the logging daemon:

      # yum install rsyslog
    4. Install the mail client for testing:

      # yum install mutt
  2. Enable the postfix and rsyslog services:

    # systemctl enable postfix rsyslog
    # systemctl restart postfix rsyslog
  3. Enable the SELinux boolean, so that pmda-postfix can access the required log files:

    # setsebool -P pcp_read_generic_logs=on
  4. Install the PMDA:

    # cd /var/lib/pcp/pmdas/postfix/
    
    # ./Install
    
    Updating the Performance Metrics Name Space (PMNS) ...
    Terminate PMDA if already installed ...
    Updating the PMCD control file, and notifying PMCD ...
    Waiting for pmcd to terminate ...
    Starting pmcd ...
    Check postfix metrics have appeared ... 7 metrics and 58 values

Verification steps

  • Verify the pmda-postfix operation:

    echo testmail | mutt root
  • Verify the available metrics:

    # pminfo postfix
    
    postfix.received
    postfix.sent
    postfix.queues.incoming
    postfix.queues.maildrop
    postfix.queues.hold
    postfix.queues.deferred
    postfix.queues.active

Additional resources

4.6. Visually tracing PCP log archives with the PCP Charts application

After recording metric data, you can replay the PCP log archives as graphs.

Using the PCP Charts application, you can:

  • Replay the data in the PCP Charts application application and use graphs to visualize the retrospective data alongside live data of the system.
  • Plot performance metric values into graphs.
  • Display multiple charts simultaneously.

The metrics are sourced from one or more live hosts with alternative options to use metric data from PCP log archives as a source of historical data.

Following are the several ways to customize the PCP Charts application interface to display the data from the performance metrics:

  • line plot
  • bar graphs
  • utilization graphs

Prerequisites

Procedure

  1. Launch the PCP Charts application from the command line:

    # pmchart

    pmchart started

    The pmtime server settings are located at the bottom. The start and pause button allows you to control:

    • The interval in which PCP polls the metric data
    • The date and time for the metrics of historical data
  2. Go to File → New Chart to select metric from both the local machine and remote machines by specifying their host name or address. Advanced configuration options include the ability to manually set the axis values for the chart, and to manually choose the color of the plots.
  3. Record the views created in the PCP Charts application:

    Following are the options to take images or record the views created in the PCP Charts application:

    • Click File → Export to save an image of the current view.
    • Click Record → Start to start a recording. Click Record → Stop to stop the recording. After stopping the recording, the recorded metrics are archived to be viewed later.
  4. Optional: In the PCP Charts application, the main configuration file, known as the view, allows the metadata associated with one or more charts to be saved. This metadata describes all chart aspects, including the metrics used and the chart columns. Save the custom view configuration by clicking File → Save View, and load the view configuration later. The following example of the PCP Charts application view configuration file describes a stacking chart graph showing the total number of bytes read and written to the given XFS file system loop1:

    #kmchart
    version 1
    
    chart title "Filesystem Throughput /loop1" style stacking antialiasing off
        plot legend "Read rate"   metric xfs.read_bytes   instance  "loop1"
        plot legend "Write rate"  metric xfs.write_bytes  instance  "loop1"

Additional resources

4.7. XFS file system performance analysis with PCP

The XFS PMDA ships as part of the pcp package and is enabled by default during the installation. It is used to gather performance metric data of XFS file systems in PCP.

4.7.1. Installing XFS PMDA manually

If the XFS PMDA is not listed in PCP configuration readout, install the PMDA agent manually.

Procedure

  1. Navigate to the xfs directory:

    # cd /var/lib/pcp/pmdas/xfs/
  2. Install the XFS PMDA manually:

    xfs]# ./Install
    
    You will need to choose an appropriate configuration for install of
    the “xfs” Performance Metrics Domain Agent (PMDA).
    
      collector     collect performance statistics on this system
      monitor       allow this system to monitor local and/or remote systems
      both          collector and monitor configuration for this system
    
    Please enter c(ollector) or m(onitor) or (both) [b]
    Updating the Performance Metrics Name Space (PMNS) ...
    Terminate PMDA if already installed ...
    Updating the PMCD control file, and notifying PMCD ...
    Waiting for pmcd to terminate ...
    Starting pmcd ...
    Check xfs metrics have appeared ... 149 metrics and 149 values
  3. Select the intended PMDA role by entering c for collector, m for monitor, or b for both. The PMDA installation script prompts you to specify one of the following PMDA roles:

    • The collector role allows the collection of performance metrics on the current system
    • The monitor role allows the system to monitor local systems, remote systems, or both

      The default option is both collector and monitor, which allows the XFS PMDA to operate correctly in most scenarios.

Additional resources

4.7.2. Examining XFS performance metrics with pminfo

The pminfo tool displays information about the available performance metrics. This procedure displays a list of all available metrics provided by the XFS PMDA.

Prerequisites

Procedure

  1. Display the list of all available metrics provided by the XFS PMDA:

    # pminfo xfs
  2. Display information for the individual metrics. The following examples examine specific XFS read and write metrics using the pminfo tool:

    • Display a short description of the xfs.write_bytes metric:

      # pminfo --oneline xfs.write_bytes
      
      xfs.write_bytes [number of bytes written in XFS file system write operations]
    • Display a long description of the xfs.read_bytes metric:

      # pminfo --helptext xfs.read_bytes
      
      xfs.read_bytes
      Help:
      This is the number of bytes read via read(2) system calls to files in
      XFS file systems. It can be used in conjunction with the read_calls
      count to calculate the average size of the read operations to file in
      XFS file systems.
    • Obtain the current performance value of the xfs.read_bytes metric:

      # pminfo --fetch xfs.read_bytes
      
      xfs.read_bytes
          value 4891346238

Additional resources

4.7.3. Resetting XFS performance metrics with pmstore

With PCP, you can modify the values of certain metrics, especially if the metric acts as a control variable, such as the xfs.control.reset metric. To modify a metric value, use the pmstore tool.

Prerequisites

Procedure

  1. Display the value of a metric:

    $ pminfo -f xfs.write
    
    xfs.write
        value 325262
  2. Reset all the XFS metrics:

    # pmstore xfs.control.reset 1
    
    xfs.control.reset old value=0 new value=1
  3. View the information after resetting the metric:

    $ pminfo --fetch xfs.write
    
    xfs.write
        value 0

4.7.4. Examining XFS metrics available per file system

PCP enables XFS PMDA to allow the reporting of certain XFS metrics per each of the mounted XFS file systems. This makes it easier to pinpoint specific mounted file system issues and evaluate performance. The pminfo command provides per-device XFS metrics for each mounted XFS file system.

Prerequisites

Procedure

  • Obtain per-device XFS metrics with pminfo:

    # pminfo --fetch --oneline xfs.perdev.read xfs.perdev.write
    
    xfs.perdev.read [number of XFS file system read operations]
    inst [0 or "loop1"] value 0
    inst [0 or "loop2"] value 0
    
    xfs.perdev.write [number of XFS file system write operations]
    inst [0 or "loop1"] value 86
    inst [0 or "loop2"] value 0

4.8. System services distributed with PCP

Name

Description

pmcd

The Performance Metric Collector Daemon (PMCD).

pmie

The Performance Metrics Inference Engine.

pmlogger

The performance metrics logger.

pmmgr

Manages a collection of PCP daemons for a set of discovered local and remote hosts running the Performance Metric Collector Daemon (PMCD) according to zero or more configuration directories.

pmproxy

The Performance Metric Collector Daemon (PMCD) proxy server.

pmwebd

Binds a subset of the Performance Co-Pilot client API to RESTful web applications using the HTTP protocol.

4.9. Tools distributed with PCP

Name

Description

pcp

Displays the current status of a Performance Co-Pilot installation.

pcp-atop

Shows the system-level occupation of the most critical hardware resources from the performance point of view: CPU, memory, disk, and network.

pcp-dstat

Displays metrics of one system at a time. To display metrics of multiple systems, use --host option.

pmchart

Plots performance metrics values available through the facilities of the Performance Co-Pilot.

pmclient

Displays high-level system performance metrics by using the Performance Metrics Application Programming Interface (PMAPI).

pmcollectl

Collects and displays system-level data, either from a live system or from a Performance Co-Pilot archive file.

pmconfig

Displays the values of configuration parameters.

pmdbg

Displays available Performance Co-Pilot debug control flags and their values.

pmdiff

Compares the average values for every metric in either one or two archives, in a given time window, for changes that are likely to be of interest when searching for performance regressions.

pmdumplog

Displays control, metadata, index, and state information from a Performance Co-Pilot archive file.

pmdumptext

Outputs the values of performance metrics collected live or from a Performance Co-Pilot archive.

pmerr

Displays available Performance Co-Pilot error codes and their corresponding error messages.

pmfind

Finds PCP services on the network.

pmie

An inference engine that periodically evaluates a set of arithmetic, logical, and rule expressions. The metrics are collected either from a live system, or from a Performance Co-Pilot archive file.

pmieconf

Displays or sets configurable pmie variables.

pminfo

Displays information about performance metrics. The metrics are collected either from a live system, or from a Performance Co-Pilot archive file.

pmiostat

Reports I/O statistics for SCSI devices (by default) or device-mapper devices (with the -x dm option).

pmlc

Interactively configures active pmlogger instances.

pmlogcheck

Identifies invalid data in a Performance Co-Pilot archive file.

pmlogconf

Creates and modifies a pmlogger configuration file.

pmloglabel

Verifies, modifies, or repairs the label of a Performance Co-Pilot archive file.

pmlogsummary

Calculates statistical information about performance metrics stored in a Performance Co-Pilot archive file.

pmprobe

Determines the availability of performance metrics.

pmrep

Reports on selected, easily customizable, performance metrics values.

pmsocks

Allows access to a Performance Co-Pilot hosts through a firewall.

pmstat

Periodically displays a brief summary of system performance.

pmstore

Modifies the values of performance metrics.

pmtrace

Provides a command line interface to the trace Performance Metrics Domain Agent (PMDA).

pmval

Displays the current value of a performance metric.

4.10. PCP metric groups for XFS

Metric Group

Metrics provided

xfs.*

General XFS metrics including the read and write operation counts, read and write byte counts. Along with counters for the number of times inodes are flushed, clustered and number of failure to cluster.

xfs.allocs.*

xfs.alloc_btree.*

Range of metrics regarding the allocation of objects in the file system, these include number of extent and block creations/frees. Allocation tree lookup and compares along with extend record creation and deletion from the btree.

xfs.block_map.*

xfs.bmap_tree.*

Metrics include the number of block map read/write and block deletions, extent list operations for insertion, deletions and lookups. Also operations counters for compares, lookups, insertions and deletion operations from the blockmap.

xfs.dir_ops.*

Counters for directory operations on XFS file systems for creation, entry deletions, count of “getdent” operations.

xfs.transactions.*

Counters for the number of meta-data transactions, these include the count for the number of synchronous and asynchronous transactions along with the number of empty transactions.

xfs.inode_ops.*

Counters for the number of times that the operating system looked for an XFS inode in the inode cache with different outcomes. These count cache hits, cache misses, and so on.

xfs.log.*

xfs.log_tail.*

Counters for the number of log buffer writes over XFS file sytems includes the number of blocks written to disk. Metrics also for the number of log flushes and pinning.

xfs.xstrat.*

Counts for the number of bytes of file data flushed out by the XFS flush deamon along with counters for number of buffers flushed to contiguous and non-contiguous space on disk.

xfs.attr.*

Counts for the number of attribute get, set, remove and list operations over all XFS file systems.

xfs.quota.*

Metrics for quota operation over XFS file systems, these include counters for number of quota reclaims, quota cache misses, cache hits and quota data reclaims.

xfs.buffer.*

Range of metrics regarding XFS buffer objects. Counters include the number of requested buffer calls, successful buffer locks, waited buffer locks, miss_locks, miss_retries and buffer hits when looking up pages.

xfs.btree.*

Metrics regarding the operations of the XFS btree.

xfs.control.reset

Configuration metrics which are used to reset the metric counters for the XFS stats. Control metrics are toggled by means of the pmstore tool.

4.11. Per-device PCP metric groups for XFS

Metric Group

Metrics provided

xfs.perdev.*

General XFS metrics including the read and write operation counts, read and write byte counts. Along with counters for the number of times inodes are flushed, clustered and number of failure to cluster.

xfs.perdev.allocs.*

xfs.perdev.alloc_btree.*

Range of metrics regarding the allocation of objects in the file system, these include number of extent and block creations/frees. Allocation tree lookup and compares along with extend record creation and deletion from the btree.

xfs.perdev.block_map.*

xfs.perdev.bmap_tree.*

Metrics include the number of block map read/write and block deletions, extent list operations for insertion, deletions and lookups. Also operations counters for compares, lookups, insertions and deletion operations from the blockmap.

xfs.perdev.dir_ops.*

Counters for directory operations of XFS file systems for creation, entry deletions, count of “getdent” operations.

xfs.perdev.transactions.*

Counters for the number of meta-data transactions, these include the count for the number of synchronous and asynchronous transactions along with the number of empty transactions.

xfs.perdev.inode_ops.*

Counters for the number of times that the operating system looked for an XFS inode in the inode cache with different outcomes. These count cache hits, cache misses, and so on.

xfs.perdev.log.*

xfs.perdev.log_tail.*

Counters for the number of log buffer writes over XFS filesytems includes the number of blocks written to disk. Metrics also for the number of log flushes and pinning.

xfs.perdev.xstrat.*

Counts for the number of bytes of file data flushed out by the XFS flush deamon along with counters for number of buffers flushed to contiguous and non-contiguous space on disk.

xfs.perdev.attr.*

Counts for the number of attribute get, set, remove and list operations over all XFS file systems.

xfs.perdev.quota.*

Metrics for quota operation over XFS file systems, these include counters for number of quota reclaims, quota cache misses, cache hits and quota data reclaims.

xfs.perdev.buffer.*

Range of metrics regarding XFS buffer objects. Counters include the number of requested buffer calls, successful buffer locks, waited buffer locks, miss_locks, miss_retries and buffer hits when looking up pages.

xfs.perdev.btree.*

Metrics regarding the operations of the XFS btree.

Chapter 5. Setting up graphical representation of PCP metrics

Using a combination of redis, pcp, bpftrace, vector and grafana provides graphs, based on the live data or data collected by Performance Co-Pilot (PCP). It allows to access graphs of PCP metrics using a web browser.

  • The PCP is a generic framework which collects, monitors, analyzes, and stores performance related metrics. For more information about the PCP and its components, see Monitoring performance with Performance Co-Pilot.
  • Redis is an in-memory-database. It is used to store data from the archived files which is easily accessible for the graph generation by the Grafana application.
  • Bpftrace allows access to the live data from sources which are not available as normal data from the pmlogger or archives.
  • Vector provides access to the live data but does not provide access to data from past.
  • Grafana generates graphs that are accessible from a browser. The grafana-server is a component that listens, by default, on all interfaces, and provides web services accessed through the web browser. The grafana-pcp plugin interacts with the pmproxy protocol in the backend.

5.1. Setting up PCP on a system

This procedure describes how to set up PCP on a system with the pcp-zeroconf package. Once the pcp-zeroconf package is installed, the system records the default set of metrics into archived files.

Procedure

  • Install the pcp-zeroconf package:

    # yum install pcp-zeroconf

Verification steps

  • Ensure that the pmlogger service is active, and starts archiving the metrics:

    # pcp | grep pmlogger
     pmlogger: primary logger: /var/log/pcp/pmlogger/localhost.localdomain/20200401.00.12

Additional resources

5.2. Setting up a grafana-server

This procedure describes how to set up a grafana-server. It is a back-end server for the Grafana dashbaord. The grafana-server component listens on the interface, and provides web services which are accessed via browser.

Prerequisites

Procedure

  1. Install the following packages:

    # yum install redis grafana grafana-pcp
  2. Restart and enable the following components:

    # systemctl restart redis pmproxy grafana-server
    # systemctl enable redis pmproxy grafana-server

Verification steps

  • Ensure that the grafana-server is listening and responding to requests:

    # ss -ntlp | grep 3000
    LISTEN  0  128  *:3000  *:*  users:(("grafana-server",pid=19522,fd=7))

Additional resources

  • The pmproxy man page.
  • The grafana-server man page.

5.3. Accessing the Grafana web UI

This procedure describes how to access the Grafana web interface and generate graphs using different types of data sources.

Prerequisites

Procedure

  1. On the client system, open a browser and access the grafana-server on port 3000, using http://192.0.2.0:3000 link.

    Replace 192.0.2.0 with your machine IP.

  2. For the first login, enter admin in both the username and password field.

    Figure 5.1. Grafana login page

    grafana login
  3. To create a secured account, Grafana prompts to set a new password.
  4. In the pane, hover on the    grafana gear icon    configuration icon > click Plugins > in the Filter by name or type, type performance co-pilot > click Performance Co-Pilot plugin and > click Enable to enable the grafana-pcp plugin.

    Figure 5.2. Home Dashboard

    grafana dashboard
    Note

    The top corner of the screen has a similar    grafana top corner settings icon    icon, but it controls the general Dashboard settings.

  5. Click the    Grafana logo    icon to view the Home Dashboard.
  6. In the Home Dashboard, click Add data source to add PCP Redis, PCP bpftrace, and PCP Vector data sources.

    For more information on how to add PCP Redis, PCP bpftrace, and PCP Vector data sources, see:

  7. Optional: In the pane, hover on the    grafana logout option icon    icon to change the Preferences or to Sign out.

Verification steps

  • Ensure that the Performance Co-Pilot plugin is enabled:

    # grafana-cli plugins ls | grep performancecopilot-pcp-app
    performancecopilot-pcp-app @ 1.0.5

Additional resources

  • The grafana-cli man page.
  • The grafana-server man page.

5.4. Adding PCP Redis as a data source

The PCP Redis data source visualizes everything which the archive contains and queries time series capability provided by the pmseries language. It analyzes data across multiple hosts. This procedure describes how to add PCP Redis as a data source and how to view the dashboard with an overview of useful metrics.

Prerequisites

Procedure

  1. Click the    Grafana logo    icon > click Add data source > in the Filter by name or type, type redis > and click PCP Redis > in the URL field, accept the given suggestion http://localhost:44322 and > click Save & Test.

    Figure 5.3. Adding PCP Redis in the data source

    pcp redis data sources
  2. In the pane, hover on the    grafana 4 queries icon    filter icon > click Manage > in the Filter Dashboard by name, type pcp redis > select PCP Redis Host Overview to see a dashboard with an overview of any useful metrics.

    Figure 5.4. PCP Redis Host Overview

    PCP redis host overview
  3. In the pane, hover on the    plus sign    icon > click Dashboard option > click Add Query > from the Query list, select the PCP Redis and > in the text field of A, enter metric, for example, kernel.all.load to visualize the kernel load graph.

    Figure 5.5. PCP Redis query

    PCP redis query

Additional resources

  • The pmseries man page.

5.5. Adding PCP bpftrace as a data source

The bpftrace agent enables system introspection using the bpftrace scripts, which uses the enhanced Berkeley Packet Filter (eBPF) to gather metrics from kernel and user-space tracepoints. This procedure describes how to add PCP bpftrace as a data source and how to view the dashboard with an overview of any useful metrics.

Prerequisites

Procedure

  1. Install the pcp-pmda-bpftrace package:

    # yum install pcp-pmda-bpftrace
  2. Edit the bpftrace.conf file and disable the authentication option:

    # vi /var/lib/pcp/pmdas/bpftrace/bpftrace.conf
    
    [authentication]
    # NB. Disabling authentication is NOT RECOMMENDED, as everyone would be
    # able to run bpftrace scripts AS ROOT!
    enabled = false
    Note

    Do not disable the authentication option in production.

  3. Install the bpftrace PMDA:

    # cd /var/lib/pcp/pmdas/bpftrace/
    # ./Install
    Updating the Performance Metrics Name Space (PMNS) ...
    Terminate PMDA if already installed ...
    Updating the PMCD control file, and notifying PMCD ...
    Check bpftrace metrics have appeared ... 7 metrics and 6 values
  4. Log into the Grafana web UI. For more information, see Section 5.3, “Accessing the Grafana web UI”.
  5. Click the    Grafana logo    icon > click Add data source > in the Filter by name or type, type bpftrace > and click PCP bpftrace > in the URL field, accept the given suggestion http://localhost:44322 and > click Save & Test.

    Figure 5.6. Adding PCP bpftrace in the data source

    pcp bpftrace data source
  6. In the pane, hover on the    grafana 4 queries icon    filter icon > click Manage > in the Filter Dashboard by name, type pcp bpftrace > select PCP bpftrace System Analysis to see a dashboard with an overview of useful metrics.

    Figure 5.7. PCP bpftrace System Analysis

    pcp bpftrace bpftrace system analysis

Additional resources

  • The pmdabpftrace man page.
  • The bpftrace man page.

5.6. Adding PCP Vector as a data source

The PCP Vector data source displays live metrics and uses the pcp metrics. It analyzes data for individual hosts. This procedure describes how to add a PCP Vector as a data source and how to view the dashboard with an overview of any useful metrics.

Prerequisites

Procedure

  1. Install the pcp-pmda-bcc package:

    # yum install pcp-pmda-bcc
  2. Install the bcc PMDA:

    # cd /var/lib/pcp/pmdas/bcc
    # ./Install
    [Wed Apr  1 00:27:48] pmdabcc(22341) Info: Initializing, currently in 'notready' state.
    [Wed Apr  1 00:27:48] pmdabcc(22341) Info: Enabled modules:
    [Wed Apr  1 00:27:48] pmdabcc(22341) Info: ['biolatency', 'sysfork',
    [...]
    Updating the Performance Metrics Name Space (PMNS) ...
    Terminate PMDA if already installed ...
    Updating the PMCD control file, and notifying PMCD ...
    Check bcc metrics have appeared ... 1 warnings, 1 metrics and 0 values
  3. Log into the Grafana web UI. For more information, see Section 5.3, “Accessing the Grafana web UI”.
  4. Click the    Grafana logo    icon > click Add data source > in the Filter by name or type, type vector > and click PCP Vector > in the URL field, accept the given suggestion http://localhost:44322 and > click Save & Test.

    Figure 5.8. Adding PCP Vector in the data source

    pcp vector
  5. In the pane, hover on the    grafana 4 queries icon    icon > click Manage > in the Filter Dashboard by name, type pcp vector > select PCP Vector Host Overview to see a dashboard with an overview of useful metrics.

    Figure 5.9. PCP Vector Host Overview

    pcp vector overview

Additional resources

  • The pmdabcc man page.

Chapter 6. Optimizing the system performance using the web console

In the web console, you can set a performance profile to optimize the performance of the system for a selected task.

6.1. Performance tuning options in the web console

Red Hat Enterprise Linux 8 provides several performance profiles that optimize the system for the following tasks:

  • Systems using the desktop
  • Throughput performance
  • Latency performance
  • Network performance
  • Low power consumption
  • Virtual machines

The tuned service optimizes system options to match the selected profile.

In the web console, you can set which performance profile your system uses.

Additional resources

6.2. Setting a performance profile in the web console

This procedure uses the web console to optimize the system performance for a selected task.

Prerequisites

Procedure

  1. Log into the RHEL 8 web console.

    For details, see Logging in to the web console.

  2. Click Overview.
  3. In the Performance Profile field, click the current performance profile.

    cockpit performance profile pf4

  4. In the Change Performance Profile dialog box, change the profile if necessary.
  5. Click Change Profile.

    cockpit performance profile change pf4

Verification steps

  • The Overview tab now shows the selected performance profile.

Chapter 7. Setting the disk scheduler

The disk scheduler is responsible for ordering the I/O requests submitted to a storage device.

You can configure the scheduler in several different ways:

7.1. Disk scheduler changes in RHEL 8

In RHEL 8, block devices support only multi-queue scheduling. This enables the block layer performance to scale well with fast solid-state drives (SSDs) and multi-core systems.

The traditional, single-queue schedulers, which were available in RHEL 7 and earlier versions, have been removed.

7.2. Available disk schedulers

The following multi-queue disk schedulers are supported in RHEL 8:

Disk schedulers

none
Implements a first-in first-out (FIFO) scheduling algorithm. It merges requests at the generic block layer through a simple last-hit cache.
mq-deadline

Attempts to provide a guaranteed latency for requests from the point at which requests reach the scheduler.

The mq-deadline scheduler sorts queued I/O requests into a read or write batch and then schedules them for execution in increasing logical block addressing (LBA) order. By default, read batches take precedence over write batches, because applications are more likely to block on read I/O operations. After mq-deadline processes a batch, it checks how long write operations have been starved of processor time and schedules the next read or write batch as appropriate.

This scheduler is suitable for most use cases, but particularly those in which read operations occur more often than write operations.

bfq

Targets desktop systems and interactive tasks.

The bfq scheduler ensures that a single application is never using all of the bandwidth. In effect, the storage device is always as responsive as if it was idle. The system does not become unresponsive when copying large files. In its default configuration, bfq focuses on delivering the lowest latency rather than achieving the maximum throughput.

bfq is based on cfq code. It does not grant the disk to each process for a fixed time slice but assigns a budget measured in number of sectors to the process.

kyber
Is intended for fast devices. The scheduler tunes itself to achieve a latency goal. You can configure the target latencies for read and synchronous write requests.

7.4. The default disk scheduler

Block devices use the default disk scheduler unless you specify another scheduler.

The kernel selects a default disk scheduler based on the type of device. The automatically selected scheduler is typically the optimal setting. If you require a different scheduler, Red Hat recommends to use udev rules or the Tuned application to configure it. Match the selected devices and switch the scheduler only for those devices.

7.5. Determining the active disk scheduler

This procedure determines which disk scheduler is currently active on a given block device.

Procedure

  • Read the content of the /sys/block/device/queue/scheduler file:

    # cat /sys/block/device/queue/scheduler
    
    [mq-deadline] kyber bfq none

    In the file name, replace device with the block device name, for example sdc.

    The active scheduler is listed in square brackets ([ ]).

7.6. Setting the disk scheduler using Tuned

This procedure creates and enables a Tuned profile that sets a given disk scheduler for selected block devices. The setting persists across system reboots.

In the following commands and configuration, replace:

  • device with the name of the block device, for example sdf
  • selected-scheduler with the disk scheduler that you want to set for the device, for example bfq

Prerequisites

Procedure

  1. Optional: Select an existing Tuned profile on which your profile will be based. For a list of available profiles, see Section 2.3, “Tuned profiles distributed with RHEL”.

    To see which profile is currently active, use:

    $ tuned-adm active
  2. Create a new directory to hold your Tuned profile:

    # mkdir /etc/tuned/my-profile
  3. Find the World Wide Name (WWN) identifier of the selected block device:

    $ udevadm info --query=property --name=/dev/device | grep WWN=
    
    ID_WWN=0x5002538d00000000
  4. Create the /etc/tuned/my-profile/tuned.conf configuration file. In the file, set the following options:

    • Optional: Include an existing profile:

      [main]
      include=existing-profile
    • Set the selected disk scheduler for the device that matches the WWN identifier:

      [disk]
      devices_udev_regex=ID_WWN=0x5002538d00000000
      elevator=selected-scheduler

      To match multiple devices in the devices_udev_regex option, enclose the identifiers in parentheses and separate them with vertical bars:

      devices_udev_regex=(ID_WWN=0x5002538d00000000)|(ID_WWN=0x1234567800000000)
  5. Enable your profile:

    # tuned-adm profile my-profile
  6. Verify that the Tuned profile is active and applied:

    $ tuned-adm active
    
    Current active profile: my-profile
    $ tuned-adm verify
    
    Verification succeeded, current system settings match the preset profile.
    See tuned log file ('/var/log/tuned/tuned.log') for details.

Additional resources

7.7. Setting the disk scheduler using udev rules

This procedure sets a given disk scheduler for specific block devices using udev rules. The setting persists across system reboots.

In the following commands and configuration, replace:

  • device with the name of the block device, for example sdf
  • selected-scheduler with the disk scheduler that you want to set for the device, for example bfq

Procedure

  1. Find the World Wide Identifier (WWID) of the block device:

    $ udevadm info --attribute-walk --name=/dev/device | grep wwid
    
        ATTRS{wwid}=="device WWID"

    An example value of device WWID is:

    t10.ATA     SAMSUNG MZNLN256HMHQ-000L7              S2WDNX0J336519
  2. Configure the udev rule. Create the /etc/udev/rules.d/99-scheduler.rules file with the following content:

    ACTION=="add|change", SUBSYSTEM=="block", ATTRS{wwid}=="device WWID", ATTR{queue/scheduler}="selected-scheduler"

    Replace device WWID with the WWID value that you found in the previous steps.

  3. Reload udev rules:

    # udevadm control --reload-rules
  4. Apply the scheduler configuration:

    # udevadm trigger --type=devices --action=change
  5. Verify the active scheduler:

    # cat /sys/block/device/queue/scheduler

7.8. Temporarily setting a scheduler for a specific disk

This procedure sets a given disk scheduler for specific block devices. The setting does not persist across system reboots.

Procedure

  • Write the name of the selected scheduler to the /sys/block/device/queue/scheduler file:

    # echo selected-scheduler > /sys/block/device/queue/scheduler

    In the file name, replace device with the block device name, for example sdc.

Verification steps

  • Verify that the scheduler is active on the device:

    # cat /sys/block/device/queue/scheduler

Chapter 8. Tuning the performance of a Samba server

This chapter describes what settings can improve the performance of Samba in certain situations, and which settings can have a negative performance impact.

Parts of this section were adopted from the Performance Tuning documentation published in the Samba Wiki. License: CC BY 4.0. Authors and contributors: See the history tab on the Wiki page.

Prerequisites

8.1. Setting the SMB protocol version

Each new SMB version adds features and improves the performance of the protocol. The recent Windows and Windows Server operating systems always supports the latest protocol version. If Samba also uses the latest protocol version, Windows clients connecting to Samba benefit from the performance improvements. In Samba, the default value of the server max protocol is set to the latest supported stable SMB protocol version.

Note

To always have the latest stable SMB protocol version enabled, do not set the server max protocol parameter. If you set the parameter manually, you will need to modify the setting with each new version of the SMB protocol, to have the latest protocol version enabled.

The following procedure explains how to use the default value in the server max protocol parameter.

Procedure

  1. Remove the server max protocol parameter from the [global] section in the /etc/samba/smb.conf file.
  2. Reload the Samba configuration

    # smbcontrol all reload-config

8.2. Tuning shares with directories that contain a large number of files

Linux supports case-sensitive file names. For this reason, Samba needs to scan directories for uppercase and lowercase file names when searching or accessing a file. You can configure a share to create new files only in lowercase or uppercase, which improves the performance.

Prerequisites

  • Samba is configured as a file server

Procedure

  1. Rename all files on the share to lowercase.

    Note

    Using the settings in this procedure, files with names other than in lowercase will no longer be displayed.

  2. Set the following parameters in the share’s section:

    case sensitive = true
    default case = lower
    preserve case = no
    short preserve case = no

    For details about the parameters, see their descriptions in the smb.conf(5) man page.

  3. Verify the /etc/samba/smb.conf file:

    # testparm
  4. Reload the Samba configuration:

    # smbcontrol all reload-config

After you applied these settings, the names of all newly created files on this share use lowercase. Because of these settings, Samba no longer needs to scan the directory for uppercase and lowercase, which improves the performance.

8.3. Settings that can have a negative performance impact

By default, the kernel in Red Hat Enterprise Linux is tuned for high network performance. For example, the kernel uses an auto-tuning mechanism for buffer sizes. Setting the socket options parameter in the /etc/samba/smb.conf file overrides these kernel settings. As a result, setting this parameter decreases the Samba network performance in most cases.

To use the optimized settings from the Kernel, remove the socket options parameter from the [global] section in the /etc/samba/smb.conf.

Chapter 9. Optimizing virtual machine performance

Virtual machines (VMs) always experience some degree of performance deterioration in comparison to the host. The following sections explain the reasons for this deterioration and provide instructions on how to minimize the performance impact of virtualization in RHEL 8, so that your hardware infrastructure resources can be used as efficiently as possible.

9.1. What influences virtual machine performance

VMs are run as user-space processes on the host. The hypervisor therefore needs to convert the host’s system resources so that the VMs can use them. As a consequence, a portion of the resources is consumed by the conversion, and the VM therefore cannot achieve the same performance efficiency as the host.

The impact of virtualization on system performance

More specific reasons for VM performance loss include:

  • Virtual CPUs (vCPUs) are implemented as threads on the host, handled by the Linux scheduler.
  • VMs do not automatically inherit optimization features, such as NUMA or huge pages, from the host kernel.
  • Disk and network I/O settings of the host might have a significant performance impact on the VM.
  • Network traffic typically travels to a VM through a software-based bridge.
  • Depending on the host devices and their models, there might be significant overhead due to emulation of particular hardware.

The severity of the virtualization impact on the VM performance is influenced by a variety factors, which include:

  • The number of concurrently running VMs.
  • The amount of virtual devices used by each VM.
  • The device types used by the VMs.

Reducing VM performance loss

RHEL 8 provides a number of features you can use to reduce the negative performance effects of virtualization. Notably:

Important

Tuning VM performance can have adverse effects on other virtualization functions. For example, it can make migrating the modified VM more difficult.

9.2. Optimizing virtual machine performance using tuned

The tuned utility is a tuning profile delivery mechanism that adapts RHEL for certain workload characteristics, such as requirements for CPU-intensive tasks or storage-network throughput responsiveness. It provides a number of tuning profiles that are pre-configured to enhance performance and reduce power consumption in a number of specific use cases. You can edit these profiles or create new profiles to create performance solutions tailored to your environment, including virtualized environments.

Red Hat recommends using the following profiles when using virtualization in RHEL 8:

  • For RHEL 8 virtual machines, use the virtual-guest profile. It is based on the generally applicable throughput-performance profile, but also decreases the swappiness of virtual memory.
  • For RHEL 8 virtualization hosts, use the virtual-host profile. This enables more aggressive writeback of dirty memory pages, which benefits the host performance.

Prerequisites

Procedure

To enable a specific tuned profile:

  1. List the available tuned profiles.

    # tuned-adm list
    
    Available profiles:
    - balanced             - General non-specialized tuned profile
    - desktop              - Optimize for the desktop use-case
    [...]
    - virtual-guest        - Optimize for running inside a virtual guest
    - virtual-host         - Optimize for running KVM guests
    Current active profile: balanced
  2. Optional: Create a new tuned profile or edit an existing tuned profile.

    For more information, see Customizing tuned profiles.

  3. Activate a tuned profile.

    # tuned-adm profile selected-profile
    • To optimize a virtualization host, use the virtual-host profile.

      # tuned-adm profile virtual-host
    • On a RHEL guest operating system, use the virtual-guest profile.

      # tuned-adm profile virtual-guest

Additional resources

9.3. Configuring virtual machine memory

To improve the performance of a virtual machine (VM), you can assign additional host RAM to the VM. Similarly, you can decrease the amount of memory allocated to a VM so the host memory can be allocated to other VMs or tasks.

To perform these actions, you can use the web console or the command-line interface.

9.3.1. Adding and removing virtual machine memory using the web console

To improve the performance of a virtual machine (VM) or to free up the host resources it is using, you can use the web console to adjust amount of memory allocated to the VM.

Prerequisites

  • The guest OS must be running the memory balloon drivers. To verify this is the case:

    1. Ensure the VM’s configuration includes the memballoon device:

      # virsh dumpxml testguest | grep memballoon
      <memballoon model='virtio'>
          </memballoon>

      If this commands displays any output and the model is not set to none, the memballoon device is present.

    2. Ensure the ballon drivers are running in the guest OS.

  • Optional: Obtain the information about the maximum memory and currently used memory for a VM. This will serve as a baseline for your changes, and also for verification.

    # virsh dominfo testguest
    Max memory:     2097152 KiB
    Used memory:    2097152 KiB
  • To use the web console to manage VMs, install the web console VM plug-in.

Procedure

  1. In the Virtual Machines interface, click a row with the name of the VMs for which you want to view and adjust the allocated memory.

    The row expands to reveal the Overview pane with basic information about the selected VMs.

  2. Click the value of the Memory line in the Overview pane.

    The Memory Adjustment dialog appears.

    virt memory cockpit
  3. Configure the virtual CPUs for the selected VM.

    • Maximum allocation - Sets the maximum amount of host memory that the VM can use for its processes. Increasing this value improves the performance potential of the VM, and reducing the value lowers the performance footprint the VM has on your host.

      Adjusting maximum memory allocation is only possible on a shut-off VM.

    • Current allocation - Sets a memory limit until the next VM reboot, up to the maximum allocation. You can use this to temporarily regulate the memory load that the VM has on the host, without changing the maximum VM allocation.
  4. Click Save.

    The memory allocation of the VM is adjusted.

Additional resources

9.3.2. Adding and removing virtual machine memory using the command-line interface

To improve the performance of a virtual machine (VM) or to free up the host resources it is using, you can use the CLI to adjust amount of memory allocated to the VM.

Prerequisites

  • The guest OS must be running the memory balloon drivers. To verify this is the case:

    1. Ensure the VM’s configuration includes the memballoon device:

      # virsh dumpxml testguest | grep memballoon
      <memballoon model='virtio'>
          </memballoon>

      If this commands displays any output and the model is not set to none, the memballoon device is present.

    2. Ensure the ballon drivers are running in the guest OS.

  • Optional: Obtain the information about the maximum memory and currently used memory for a VM. This will serve as a baseline for your changes, and also for verification.

    # virsh dominfo testguest
    Max memory:     2097152 KiB
    Used memory:    2097152 KiB

Procedure

  1. Adjust the maximum memory allocated to a VM. Increasing this value improves the performance potential of the VM, and reducing the value lowers the performance footprint the VM has on your host. Note that this change can only be performed on a shut-off VM, so adjusting a running VM requires a reboot to take effect.

    For example, to change the maximum memory that the testguest VM can use to 4096 MiB:

    # virt-xml testguest --edit --memory 4096
    Domain 'testguest' defined successfully.
    Changes will take effect after the domain is fully powered off.
  1. Optional: You can also adjust the memory currently used by the VM, up to the maximum allocation. This regulates the memory load that the VM has on the host until the next reboot, without changing the maximum VM allocation.

    # virsh setmem testguest --current 2048

Verification

  1. Confirm that the memory used by the VM has been updated:

    # virsh dominfo testguest
    Max memory:     4194304 KiB
    Used memory:    2097152 KiB
  2. Optional: If you adjusted the current VM memory, you can obtain the memory balloon statistics of the VM to evaluate how effectively it regulates its memory use.

     # virsh domstats --balloon testguest
    Domain: 'testguest'
      balloon.current=365624
      balloon.maximum=4194304
      balloon.swap_in=0
      balloon.swap_out=0
      balloon.major_fault=306
      balloon.minor_fault=156117
      balloon.unused=3834448
      balloon.available=4035008
      balloon.usable=3746340
      balloon.last-update=1587971682
      balloon.disk_caches=75444
      balloon.hugetlb_pgalloc=0
      balloon.hugetlb_pgfail=0
      balloon.rss=1005456

Additional resources

9.3.3. Additional resources

  • To increase the maximum memory of a running VM, you can attach a memory device to the VM. This is also referred to as memory hot plug. For details, see Attaching devices to virtual machines.

    Note that removing a memory device from a VM, also known as memory hot unplug, is not supported in RHEL 8, and Red Hat highly discourages its use.

9.4. Optimizing virtual machine I/O performance

The input and output (I/O) capabilities of a virtual machine (VM) can significantly limit the VM’s overall efficiency. To address this, you can optimize a VM’s I/O by configuring block I/O parameters.

9.4.1. Tuning block I/O in virtual machines

When multiple block devices are being used by one or more VMs, it might be important to adjust the I/O priority of specific virtual devices by modifying their I/O weights.

Increasing the I/O weight of a device increases its priority for I/O bandwidth, and therefore provides it with more host resources. Similarly, reducing a device’s weight makes it consume less host resources.

Note

Each device’s weight value must be within the 100 to 1000 range. Alternatively, the value can be 0, which removes that device from per-device listings.

Procedure

To display and set a VM’s block I/O parameters:

  1. Display the current <blkio> parameters for a VM:

    # virsh dumpxml VM-name

    <domain>
      [...]
      <blkiotune>
        <weight>800</weight>
        <device>
          <path>/dev/sda</path>
          <weight>1000</weight>
        </device>
        <device>
          <path>/dev/sdb</path>
          <weight>500</weight>
        </device>
      </blkiotune>
      [...]
    </domain>
  2. Edit the I/O weight of a specified device:

    # virsh blkiotune VM-name --device-weights device, I/O-weight

    For example, the following changes the weight of the /dev/sda device in the liftrul VM to 500.

    # virsh blkiotune liftbrul --device-weights /dev/sda, 500

9.4.2. Disk I/O throttling in virtual machines

When several VMs are running simultaneously, they can interfere with system performance by using excessive disk I/O. Disk I/O throttling in KVM virtualization provides the ability to set a limit on disk I/O requests sent from the VMs to the host machine. This can prevent a VM from over-utilizing shared resources and impacting the performance of other VMs.

To enable disk I/O throttling, set a limit on disk I/O requests sent from each block device attached to VMs to the host machine.

Procedure

  1. Use the virsh domblklist command to list the names of all the disk devices on a specified VM.

    # virsh domblklist rollin-coal
    Target     Source
    ------------------------------------------------
    vda        /var/lib/libvirt/images/rollin-coal.qcow2
    sda        -
    sdb        /home/horridly-demanding-processes.iso
  2. Set I/O limits for a block device attached to a VM using the virsh blkdeviotune command:

    # virsh blkdeviotune VM-name device --parameter limit

    For example, to throttle the sdb device on the rollin-coal VM to 1000 I/O operations per second and 50 MB per second throughput:

    # virsh blkdeviotune rollin-coal sdb --total-iops-sec 1000 --total-bytes-sec 52428800

Additional information

  • Disk I/O throttling can be useful in various situations, for example when VMs belonging to different customers are running on the same host, or when quality of service guarantees are given for different VMs. Disk I/O throttling can also be used to simulate slower disks.
  • I/O throttling can be applied independently to each block device attached to a VM and supports limits on throughput and I/O operations.

9.4.3. Enabling multi-queue virtio-scsi

When using virtio-scsi storage devices in your virtual machines (VMs), the multi-queue virtio-scsi feature provides improved storage performance and scalability. It enables each virtual CPU (vCPU) to have a separate queue and interrupt to use without affecting other vCPUs.

Procedure

  • To enable multi-queue virtio-scsi support for a specific VM, add the following to the VM’s XML configuration, where N is the total number of vCPU queues:

    <controller type='scsi' index='0' model='virtio-scsi'>
       <driver queues='N' />
    </controller>

9.5. Optimizing virtual machine CPU performance

Much like physical CPUs in host machines, vCPUs are critical to virtual machine (VM) performance. As a result, optimizing vCPUs can have a significant impact on the resource efficiency of your VMs. To optimize your vCPU:

  1. Adjust how many host CPUs are assigned to the VM. You can do this using the CLI or the web console.
  2. Ensure that the vCPU model is aligned with the CPU model of the host. For example, to set the testguest1 VM to use the CPU model of the host:

    # virt-xml testguest1 --edit --cpu host-model
  3. If your host machine uses Non-Uniform Memory Access (NUMA), you can also configure NUMA for its VMs. This maps the host’s CPU and memory processes onto the CPU and memory processes of the VM as closely as possible. In effect, NUMA tuning provides the vCPU with a more streamlined access to the system memory allocated to the VM, which can improve the vCPU processing effectiveness.

    For details, see Section 9.5.3, “Configuring NUMA in a virtual machine” and Section 9.5.4, “Sample vCPU performance tuning scenario”.

9.5.1. Adding and removing virtual CPUs using the command-line interface

To increase or optimize the CPU performance of a virtual machine (VM), you can add or remove virtual CPUs (vCPUs) assigned to the VM.

When performed on a running VM, this is also referred to as vCPU hot plugging and hot unplugging. However, note that vCPU hot unplug is not supported in RHEL 8, and Red Hat highly discourages its use.

Prerequisites

  • Optional: View the current state of the vCPUs in the targeted VM. For example, to display the number of vCPUs on the testguest VM:

    # virsh vcpucount testguest
    maximum      config         4
    maximum      live           2
    current      config         2
    current      live           1

    This output indicates that testguest is currently using 1 vCPU, and 1 more vCPu can be hot plugged to it to increase the VM’s performance. However, after reboot, the number of vCPUs testguest uses will change to 2, and it will be possible to hot plug 2 more vCPUs.

Procedure

  1. Adjust the maximum number of vCPUs that can be attached to a VM, which takes effect on the VM’s next boot.

    For example, to increase the maximum vCPU count for the testguest VM to 8:

    # virsh setvcpus testguest 8 --maximum --config

    Note that the maximum may be limited by the CPU topology, host hardware, the hypervisor, and other factors.

  2. Adjust the current number of vCPUs attached to a VM, up to the maximum configured in the previous step. For example:

    • To increase the number of vCPUs attached to the running testguest VM to 4:

      # virsh setvcpus testguest 4 --live

      This increases the VM’s performance and host load footprint of testguest until the VM’s next boot.

    • To permanently decrease the number of vCPUs attached to the testguest VM to 1:

      # virsh setvcpus testguest 1 --config

      This decreases the VM’s performance and host load footprint of testguest after the VM’s next boot. However, if needed, additional vCPUs can be hot plugged to the VM to temporarily increase its performance.

Verification

  • Confirm that the current state of vCPU for the VM reflects your changes.

    # virsh vcpucount testguest
    maximum      config         8
    maximum      live           4
    current      config         1
    current      live           4

Additional resources

9.5.2. Managing virtual CPUs using the web console

Using the RHEL 8 web console, you can review and configure virtual CPUs used by virtual machines (VMs) to which the web console is connected.

Prerequisites

Procedure

  1. In the Virtual Machines interface, click a row with the name of the VMs for which you want to view and configure virtual CPU parameters.

    The row expands to reveal the Overview pane with basic information about the selected VMs, including the number of virtual CPUs, and controls for shutting down and deleting the VM.

  2. Click the number of vCPUs in the Overview pane.

    The vCPU details dialog appears.

    cockpit configure vCPUs

    Note

    The warning in the vCPU details dialog only appears after the virtual CPU settings are changed.

  3. Configure the virtual CPUs for the selected VM.

    • vCPU Count - The number of vCPUs currently in use.

      Note

      The vCPU count cannot be greater than the vCPU Maximum.

    • vCPU Maximum - The maximum number of virtual CPUs that can be configured for the VM. If this value is higher than the vCPU Count, additional vCPUs can be attached to the VM.
    • Sockets - The number of sockets to expose to the VM.
    • Cores per socket - The number of cores for each socket to expose to the VM.
    • Threads per core - The number of threads for each core to expose to the VM.

      Note that the Sockets, Cores per socket, and Threads per core options adjust the CPU topology of the VM. This may be beneficial for vCPU performance and may impact the functionality of certain software in the guest OS. If a different setting is not required by your deployment, Red Hat recommends keeping the default values.

  4. Click Apply.

    The virtual CPUs for the VM are configured.

    Note

    Changes to virtual CPU settings only take effect after the VM is restarted.

Additional resources:

9.5.3. Configuring NUMA in a virtual machine

The following methods can be used to configure Non-Uniform Memory Access (NUMA) settings of a virtual machine (VM) on a RHEL 8 host.

Prerequisites

  • The host must be a NUMA-compatible machine. To detect whether this is the case, use the virsh nodeinfo command and see the NUMA cell(s) line:

    # virsh nodeinfo
    CPU model:           x86_64
    CPU(s):              48
    CPU frequency:       1200 MHz
    CPU socket(s):       1
    Core(s) per socket:  12
    Thread(s) per core:  2
    NUMA cell(s):        2
    Memory size:         67012964 KiB

    If the value of the line is 2 or greater, the host is NUMA-compatible.

Procedure

For ease of use, you can set up a VM’s NUMA configuration using automated utilities and services. However, manual NUMA setup is more likely to yield a significant performance improvement.

Automatic methods

  • Set the VM’s NUMA policy to Preferred. For example, to do so for the testguest5 VM:

    # virt-xml testguest5 --edit --vcpus placement=auto
    # virt-xml testguest5 --edit --numatune mode=preferred
  • Enable automatic NUMA balancing on the host:

    # echo 1 > /proc/sys/kernel/numa_balancing
  • Use the numad command to automatically align the VM CPU with memory resources.

    # numad

Manual methods

  1. Pin specific vCPU threads to a specific host CPU or range of CPUs. This is also possible on non-NUMA hosts and VMs, and is recommended as a safe method of vCPU performance improvement.

    For example, the following commands pin vCPU threads 0 to 5 of the testguest6 VM to host CPUs 1, 3, 5, 7, 9, and 11, respectively:

    # virsh vcpupin testguest6 0 1
    # virsh vcpupin testguest6 1 3
    # virsh vcpupin testguest6 2 5
    # virsh vcpupin testguest6 3 7
    # virsh vcpupin testguest6 4 9
    # virsh vcpupin testguest6 5 11

    Afterwards, you can verify whether this was successful:

    # virsh vcpupin testguest6
    VCPU   CPU Affinity
    ----------------------
    0      1
    1      3
    2      5
    3      7
    4      9
    5      11
  2. After pinning vCPU threads, you can also pin QEMU process threads associated with a specified VM to a specific host CPU or range of CPUs. For example, the following commands pin the QEMU process thread of testguest6 to CPUs 13 and 15, and verify this was successful:

    # virsh emulatorpin testguest6 13,15
    # virsh emulatorpin testguest6
    emulator: CPU Affinity
    ----------------------------------
           *: 13,15
  3. Finally, you can also specify which host NUMA nodes will be assigned specifically to a certain VM. This can improve the host memory usage by the VM’s vCPU. For example, the following commands set testguest6 to use host NUMA nodes 3 to 5, and verify this was successful:

    # virsh numatune testguest6 --nodeset 3-5
    # virsh numatune testguest6

Additional resources

9.5.4. Sample vCPU performance tuning scenario

To obtain the best vCPU performance possible, Red Hat recommends using manual vcpupin, emulatorpin, and numatune settings together, for example like in the following scenario.

Starting scenario

  • Your host has the following hardware specifics:

    • 2 NUMA nodes
    • 3 CPU cores on each node
    • 2 threads on each core

    The output of virsh nodeinfo of such a machine would look similar to:

    # virsh nodeinfo
    CPU model:           x86_64
    CPU(s):              12
    CPU frequency:       3661 MHz
    CPU socket(s):       2
    Core(s) per socket:  3
    Thread(s) per core:  2
    NUMA cell(s):        2
    Memory size:         31248692 KiB
  • You intend to modify an existing VM to have 8 vCPUs, which means that it will not fit in a single NUMA node.

    Therefore, you should distribute 4 vCPUs on each NUMA node and make the vCPU topology resemble the host topology as closely as possible. This means that vCPUs that run as sibling threads of a given physical CPU should be pinned to host threads on the same core. For details, see the Solution below:

Solution

  1. Obtain the information on the host topology:

    # virsh capabilities

    The output should include a section that looks similar to the following:

    <topology>
      <cells num="2">
        <cell id="0">
          <memory unit="KiB">15624346</memory>
          <pages unit="KiB" size="4">3906086</pages>
          <pages unit="KiB" size="2048">0</pages>
          <pages unit="KiB" size="1048576">0</pages>
          <distances>
            <sibling id="0" value="10" />
            <sibling id="1" value="21" />
          </distances>
          <cpus num="6">
            <cpu id="0" socket_id="0" core_id="0" siblings="0,3" />
            <cpu id="1" socket_id="0" core_id="1" siblings="1,4" />
            <cpu id="2" socket_id="0" core_id="2" siblings="2,5" />
            <cpu id="3" socket_id="0" core_id="0" siblings="0,3" />
            <cpu id="4" socket_id="0" core_id="1" siblings="1,4" />
            <cpu id="5" socket_id="0" core_id="2" siblings="2,5" />
          </cpus>
        </cell>
        <cell id="1">
          <memory unit="KiB">15624346</memory>
          <pages unit="KiB" size="4">3906086</pages>
          <pages unit="KiB" size="2048">0</pages>
          <pages unit="KiB" size="1048576">0</pages>
          <distances>
            <sibling id="0" value="21" />
            <sibling id="1" value="10" />
          </distances>
          <cpus num="6">
            <cpu id="6" socket_id="1" core_id="3" siblings="6,9" />
            <cpu id="7" socket_id="1" core_id="4" siblings="7,10" />
            <cpu id="8" socket_id="1" core_id="5" siblings="8,11" />
            <cpu id="9" socket_id="1" core_id="3" siblings="6,9" />
            <cpu id="10" socket_id="1" core_id="4" siblings="7,10" />
            <cpu id="11" socket_id="1" core_id="5" siblings="8,11" />
          </cpus>
        </cell>
      </cells>
    </topology>
  2. Optional: Test the performance of the VM using the applicable tools and utilities.
  3. Set up and mount 1 GiB huge pages on the host:

    1. Add the following line to the host’s kernel command line:

      default_hugepagesz=1G hugepagesz=1G
    2. Create the /etc/systemd/system/hugetlb-gigantic-pages.service file with the following content:

      [Unit]
      Description=HugeTLB Gigantic Pages Reservation
      DefaultDependencies=no
      Before=dev-hugepages.mount
      ConditionPathExists=/sys/devices/system/node
      ConditionKernelCommandLine=hugepagesz=1G
      
      [Service]
      Type=oneshot
      RemainAfterExit=yes
      ExecStart=/etc/systemd/hugetlb-reserve-pages.sh
      
      [Install]
      WantedBy=sysinit.target
    3. Create the /etc/systemd/hugetlb-reserve-pages.sh file with the following content:

      #!/bin/sh
      
      nodes_path=/sys/devices/system/node/
      if [ ! -d $nodes_path ]; then
      	echo "ERROR: $nodes_path does not exist"
      	exit 1
      fi
      
      reserve_pages()
      {
      	echo $1 > $nodes_path/$2/hugepages/hugepages-1048576kB/nr_hugepages
      }
      
      reserve_pages 4 node1
      reserve_pages 4 node2

      This reserves four 1GiB huge pages from node1 and four 1GiB huge pages from node2.

    4. Make the script created in the previous step executable:

      # chmod +x /etc/systemd/hugetlb-reserve-pages.sh
    5. Enable huge page reservation on boot:

      # systemctl enable hugetlb-gigantic-pages
  4. Use the virsh edit command to edit the XML configuration of the VM you wish to optimize, in this example super-VM:

    # virsh edit super-vm
  5. Adjust the XML configuration of the VM in the following way:

    1. Set the VM to use 8 static vCPUs. Use the <vcpu/> element to do this.
    2. Pin each of the vCPU threads to the corresponding host CPU threads that it mirrors in the topology. To do so, use the <vcpupin/> elements in the <cputune> section.

      Note that, as shown by the virsh capabilities utility above, host CPU threads are not ordered sequentially in their respective cores. In addition, the vCPU threads should be pinned to the highest available set of host cores on the same NUMA node. For a table illustration, see the Additional Resources section below.

      The XML configuration for steps a. and b. can look similar to:

      <cputune>
        <vcpupin vcpu='0' cpuset='1'/>
        <vcpupin vcpu='1' cpuset='4'/>
        <vcpupin vcpu='2' cpuset='2'/>
        <vcpupin vcpu='3' cpuset='5'/>
        <vcpupin vcpu='4' cpuset='7'/>
        <vcpupin vcpu='5' cpuset='10'/>
        <vcpupin vcpu='6' cpuset='8'/>
        <vcpupin vcpu='7' cpuset='11'/>
        <emulatorpin cpuset='6,9'/>
      </cputune>
    3. Set the VM to use 1 GiB huge pages:

      <memoryBacking>
        <hugepages>
          <page size='1' unit='GiB'/>
        </hugepages>
      </memoryBacking>
    4. Configure the VM’s NUMA nodes to use memory from the corresponding NUMA nodes on the host. To do so, use the <memnode/> elements in the <numatune/> section:

      <numatune>
        <memory mode="preferred" nodeset="1"/>
        <memnode cellid="0" mode="strict" nodeset="0"/>
        <memnode cellid="1" mode="strict" nodeset="1"/>
      </numatune>
    5. Ensure the CPU mode is set to host-passthrough, and that the CPU uses cache in passthrough mode:

      <cpu mode="host-passthrough">
        <topology sockets="2" cores="2" threads="2"/>
        <cache mode="passthrough"/>
  6. The resulting XML configuration of the VM should include a section similar to the following:

    [...]
      <memoryBacking>
        <hugepages>
          <page size='1' unit='GiB'/>
        </hugepages>
      </memoryBacking>
      <vcpu placement='static'>8</vcpu>
      <cputune>
        <vcpupin vcpu='0' cpuset='1'/>
        <vcpupin vcpu='1' cpuset='4'/>
        <vcpupin vcpu='2' cpuset='2'/>
        <vcpupin vcpu='3' cpuset='5'/>
        <vcpupin vcpu='4' cpuset='7'/>
        <vcpupin vcpu='5' cpuset='10'/>
        <vcpupin vcpu='6' cpuset='8'/>
        <vcpupin vcpu='7' cpuset='11'/>
        <emulatorpin cpuset='6,9'/>
      </cputune>
      <numatune>
        <memory mode="preferred" nodeset="1"/>
        <memnode cellid="0" mode="strict" nodeset="0"/>
        <memnode cellid="1" mode="strict" nodeset="1"/>
      </numatune>
      <cpu mode="host-passthrough">
        <topology sockets="2" cores="2" threads="2"/>
        <cache mode="passthrough"/>
        <numa>
          <cell id="0" cpus="0-3" memory="2" unit="GiB">
            <distances>
              <sibling id="0" value="10"/>
              <sibling id="1" value="21"/>
            </distances>
          </cell>
          <cell id="1" cpus="4-7" memory="2" unit="GiB">
            <distances>
              <sibling id="0" value="21"/>
              <sibling id="1" value="10"/>
            </distances>
          </cell>
        </numa>
      </cpu>
    </domain>
  7. Optional: Test the performance of the VM using the applicable tools and utilities to evaluate the impact of the VM’s optimization.

Additional resources

  • The following tables illustrate the connections between the vCPUs and the host CPUs they should be pinned to:

    Table 9.1. Host topology

    CPU threads

    0

    3

    1

    4

    2

    5

    6

    9

    7

    10

    8

    11

    Cores

    0

    1

    2

    3

    4

    5

    Sockets

    0

    1

    NUMA nodes

    0

    1

    Table 9.2. VM topology

    vCPU threads

    0

    1

    2

    3

    4

    5

    6

    7

    Cores

    0

    1

    2

    3

    Sockets

    0

    1

    NUMA nodes

    0

    1

    Table 9.3. Combined host and VM topology

    vCPU threads

     

    0

    1

    2

    3

     

    4

    5

    6

    7

    Host CPU threads

    0

    3

    1

    4

    2

    5

    6

    9

    7

    10

    8

    11

    Cores

    0

    1

    2

    3

    4

    5

    Sockets

    0

    1

    NUMA nodes

    0

    1

    In this scenario, there are 2 NUMA nodes and 8 vCPUs. Therefore, 4 vCPU threads should be pinned to each node.

    In addition, Red Hat recommends leaving at least a single CPU thread available on each node for host system operations.

    Because in this example, each NUMA node houses 3 cores, each with 2 host CPU threads, the set for node 0 translates as follows:

    <vcpupin vcpu='0' cpuset='1'/>
    <vcpupin vcpu='1' cpuset='4'/>
    <vcpupin vcpu='2' cpuset='2'/>
    <vcpupin vcpu='3' cpuset='5'/>

9.6. Optimizing virtual machine network performance

Due to the virtual nature of a VM’s network interface card (NIC), the VM loses a portion of its allocated host network bandwidth, which can reduce the overall workload efficiency of the VM. The following tips can minimize the negative impact of virtualization on the virtual NIC (vNIC) throughput.

Procedure

Use any of the following methods and observe if it has a beneficial effect on your VM network performance:

Enable the vhost_net module

On the host, ensure the vhost_net kernel feature is enabled:

# lsmod | grep vhost
vhost_net              32768  1
vhost                  53248  1 vhost_net
tap                    24576  1 vhost_net
tun                    57344  6 vhost_net

If the output of this command is blank, enable the vhost_net kernel module:

# modprobe vhost_net
Set up multi-queue virtio-net

To set up the multi-queue virtio-net feature for a VM, use the virsh edit command to edit to the XML configuration of the VM. In the XML, add the following to the <devices> section, and replace N with the number of vCPUs in the VM, up to 16:

<interface type='network'>
      <source network='default'/>
      <model type='virtio'/>
      <driver name='vhost' queues='N'/>
</interface>

If the VM is running, restart it for the changes to take effect.

Set up vhost zero-copy transmit

If using a network with large packet size, enable the vhost zero-copy transmit feature.

Note that this feature only improves the performance when transmitting large packets between a guest network and an external network. It does not affect performance for guest-to-guest and guest-to-host workloads. In addition, it is likely to have a negative impact on the performance of small packet workloads.

Also, enabling zero-copy transmit can cause head-of-line blocking of packets, which may create a potential security risk.

To enable vhost zero-copy transmit:

  1. On the host, disable the vhost-net kernel module:

    # modprobe -r vhost_net
  2. Re-enable the vhost-net module with the zero-copy parameter turned on:

    # modprobe vhost-net experimental_zcopytx=1
  3. Check whether zero-copy transmit was enabled successfully:

    # cat /sys/module/vhost_net/parameters/experimental_zcopytx
    1
Batching network packets

In Linux VM configurations with a long transmission path, batching packets before submitting them to the kernel may improve cache utilization. To set up packet batching, use the following command on the host, and replace tap0 with the name of the network interface that the VMs use:

# ethtool -C tap0 rx-frames 128
SR-IOV
If your host NIC supports SR-IOV, use SR-IOV device assignment for your vNICs. For more information, see Managing SR-IOV devices.

Additional resources

9.7. Virtual machine performance monitoring tools

To identify what consumes the most VM resources and which aspect of VM performance needs optimization, performance diagnostic tools, both general and VM-specific, can be used.

Default OS performance monitoring tools

For standard performance evaluation, you can use the utilities provided by default by your host and guest operating systems:

  • On your RHEL 8 host, as root, use the top utility or the system monitor application, and look for qemu and virt in the output. This shows how much host system resources your VMs are consuming.

    • If the monitoring tool displays that any of the qemu or virt processes consume a large portion of the host CPU or memory capacity, use the perf utility to investigate. For details, see below.
    • In addition, if a vhost_net thread process, named for example vhost_net-1234, is displayed as consuming an excessive amount of host CPU capacity, consider using virtual network optimization features, such as multi-queue virtio-net.
  • On the guest operating system, use performance utilities and applications available on the system to evaluate which processes consume the most system resources.

    • On Linux systems, you can use the top utility.
    • On Windows systems, you can use the Task Manager application.

perf kvm

You can use the perf utility to collect and analyze virtualization-specific statistics about the performance of your RHEL 8 host. To do so:

  1. On the host, install the perf package:

    # yum install perf
  2. Use the perf kvm stat command to display perf statistics for your virtualization host:

    • For real-time monitoring of your hypervisor, use the perf kvm stat live command.
    • To log the perf data of your hypervisor over a period of time, activate the logging using the perf kvm stat record command. After the command is canceled or interrupted, the data is saved in the perf.data.guest file, which can be analyzed using the perf kvm stat report command.
  3. Analyze the perf output for types of VM-EXIT events and their distribution. For example, the PAUSE_INSTRUCTION events should be infrequent, but in the following output, the high occurrence of this event suggests that the host CPUs are not handling the running vCPUs well. In such a scenario, consider shutting down some of your active VMs, removing vCPUs from these VMs, or tuning the performance of the vCPUs.

    # perf kvm stat report
    
    Analyze events for all VMs, all VCPUs:
    
    
                 VM-EXIT    Samples  Samples%     Time%    Min Time    Max Time         Avg time
    
      EXTERNAL_INTERRUPT     365634    31.59%    18.04%      0.42us  58780.59us    204.08us ( +-   0.99% )
               MSR_WRITE     293428    25.35%     0.13%      0.59us  17873.02us      1.80us ( +-   4.63% )
        PREEMPTION_TIMER     276162    23.86%     0.23%      0.51us  21396.03us      3.38us ( +-   5.19% )
       PAUSE_INSTRUCTION     189375    16.36%    11.75%      0.72us  29655.25us    256.77us ( +-   0.70% )
                     HLT      20440     1.77%    69.83%      0.62us  79319.41us  14134.56us ( +-   0.79% )
                  VMCALL      12426     1.07%     0.03%      1.02us   5416.25us      8.77us ( +-   7.36% )
           EXCEPTION_NMI         27     0.00%     0.00%      0.69us      1.34us      0.98us ( +-   3.50% )
           EPT_MISCONFIG          5     0.00%     0.00%      5.15us     10.85us      7.88us ( +-  11.67% )
    
    Total Samples:1157497, Total events handled time:413728274.66us.

    Other event types that can signal problems in the output of perf kvm stat include:

For more information on using perf to monitor virtualization performance, see the perf-kvm man page.

numastat

To see the current NUMA configuration of your system, you can use the numastat utility, which is provided by installing the numactl package.

The following shows a host with 4 running VMs, each obtaining memory from multiple NUMA nodes. This is not optimal for vCPU performance, and warrants adjusting:

# numastat -c qemu-kvm

Per-node process memory usage (in MBs)
PID              Node 0 Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 Node 7 Total
---------------  ------ ------ ------ ------ ------ ------ ------ ------ -----
51722 (qemu-kvm)     68     16    357   6936      2      3    147    598  8128
51747 (qemu-kvm)    245     11      5     18   5172   2532      1     92  8076
53736 (qemu-kvm)     62    432   1661    506   4851    136     22    445  8116
53773 (qemu-kvm)   1393      3      1      2     12      0      0   6702  8114
---------------  ------ ------ ------ ------ ------ ------ ------ ------ -----
Total              1769    463   2024   7462  10037   2672    169   7837 32434

In contrast, the following shows memory being provided to each VM by a single node, which is significantly more efficient.

# numastat -c qemu-kvm

Per-node process memory usage (in MBs)
PID              Node 0 Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 Node 7 Total
---------------  ------ ------ ------ ------ ------ ------ ------ ------ -----
51747 (qemu-kvm)      0      0      7      0   8072      0      1      0  8080
53736 (qemu-kvm)      0      0      7      0      0      0   8113      0  8120
53773 (qemu-kvm)      0      0      7      0      0      0      1   8110  8118
59065 (qemu-kvm)      0      0   8050      0      0      0      0      0  8051
---------------  ------ ------ ------ ------ ------ ------ ------ ------ -----
Total                 0      0   8072      0   8072      0   8114   8110 32368

Chapter 10. Managing power consumption with PowerTOP

As a system administrator, you can use the PowerTOP tool to analyze and manage power consumption.

10.1. The purpose of PowerTOP

PowerTOP is a program that diagnoses issues related to power consumption and provides suggestions on how to extend battery lifetime.

The PowerTOP tool can provide an estimate of the total power usage of the system and also individual power usage for each process, device, kernel worker, timer, and interrupt handler. The tool can also identify specific components of kernel and user-space applications that frequently wake up the CPU.

Red Hat Enterprise Linux 8 uses version 2.x of PowerTOP.

10.2. Using PowerTOP

Prerequisites

  • To be able to use PowerTOP, make sure that the powertop package has been installed on your system:

    # yum install powertop

10.2.1. Starting PowerTOP

Procedure

  • To run PowerTOP, use the following command:

    # powertop
Important

Laptops should run on battery power when running the powertop command.

10.2.2. Calibrating PowerTOP

Procedure

  1. On a laptop, you can calibrate the power estimation engine by running the following command:

    # powertop --calibrate
  2. Let the calibration finish without interacting with the machine during the process.

    Calibration takes time because the process performs various tests, cycles through brightness levels and switches devices on and off.

  3. When the calibration process is completed, PowerTOP starts as normal. Let it run for approximately an hour to collect data.

    When enough data is collected, power estimation figures will be displayed in the first column of the output table.

Note

Note that powertop --calibrate can only be used on laptops.

10.2.3. Setting the measuring interval

By default, PowerTOP takes measurements in 20 seconds intervals.

If you want to change this measuring frequency, use the following procedure:

Procedure

  • Run the powertop command with the --time option:

    # powertop --time=time in seconds

10.3. PowerTOP statistics

While it runs, PowerTOP gathers statistics from the system.

PowerTOP's output provides multiple tabs:

  • Overview
  • Idle stats
  • Frequency stats
  • Device stats
  • Tunables

You can use the Tab and Shift+Tab keys to cycle through these tabs.

10.3.1. The Overview tab

In the Overview tab, you can view a list of the components that either send wakeups to the CPU most frequently or consume the most power. The items within the Overview tab, including processes, interrupts, devices, and other resources, are sorted according to their utilization.

The adjacent columns within the Overview tab provide the following pieces of information:

Usage
Power estimation of how the resource is being used.
Events/s
Wakeups per second. The number of wakeups per second indicates how efficiently the services or the devices and drivers of the kernel are performing. Less wakeups means that less power is consumed. Components are ordered by how much further their power usage can be optimized.
Category
Classification of the component; such as process, device, or timer.
Description
Description of the component.

If properly calibrated, a power consumption estimation for every listed item in the first column is shown as well.

Apart from this, the Overview tab includes the line with summary statistics such as:

  • Total power consumption
  • Remaining battery life (only if applicable)
  • Summary of total wakeups per second, GPU operations per second, and virtual file system operations per second

10.3.2. The Idle stats tab

The Idle stats tab shows usage of C-states for all processors and cores, while the Frequency stats tab shows usage of P-states including the Turbo mode, if applicable, for all processors and cores. The duration of C- or P-states is an indication of how well the CPU usage has been optimized. The longer the CPU stays in the higher C- or P-states (for example C4 is higher than C3), the better the CPU usage optimization is. Ideally, residency is 90% or more in the highest C- or P-state when the system is idle.

10.3.3. The Device stats tab

The Device stats tab provides similar information to the Overview tab but only for devices.

10.3.4. The Tunables tab

The Tunables tab contains PowerTOP's suggestions for optimizing the system for lower power consumption.

Use the up and down keys to move through suggestions, and the enter key to toggle the suggestion on or off.

Figure 10.1. PowerTOP output

powertop output n

Additional resources

For more details on PowerTOP, see PowerTOP’s home page.

10.4. Generating an HTML output

Apart from the powertop’s output in terminal, you can also generate an HTML report.

Procedure

  • Run the powertop command with the --html option:

    # powertop --html=htmlfile.html

    Replace the htmlfile.html parameter with the required name for the output file.

10.5. Optimizing power consumption

To optimize power consumption, you can use either the powertop service or the powertop2tuned utility.

10.5.1. Optimizing power consumption using the powertop service

You can use the powertop service to automatically enable all PowerTOP's suggestions from the Tunables tab on the boot:

Procedure

  • Enable the powertop service:

    # systemctl enable powertop

10.5.2. The powertop2tuned utility

The powertop2tuned utility allows you to create custom Tuned profiles from PowerTOP suggestions.

By default, powertop2tuned creates profiles in the /etc/tuned/ directory, and bases the custom profile on the currently selected Tuned profile. For safety reasons, all PowerTOP tunings are initially disabled in the new profile.

To enable the tunings, you can:

  • Uncomment them in the /etc/tuned/profile_name/tuned.conf file.
  • Use the --enable or -e option to generate a new profile that enables most of the tunings suggested by PowerTOP.

    Certain potentially problematic tunings, such as the USB autosuspend, are disabled by default and need to be uncommented manually.

10.5.3. Optimizing power consumption using the powertop2tuned utility

Prerequisites

  • The powertop2tuned utility is installed on the system:

    # yum install tuned-utils

Procedure

  1. Create a custom profile:

    # powertop2tuned new_profile_name
  2. Activate the new profile:

    # tuned-adm profile new_profile_name

Additional information

  • For a complete list of options that powertop2tuned supports, use:

    $ powertop2tuned --help

10.5.4. Comparison of powertop.service and powertop2tuned

Optimizing power consumption with powertop2tuned is preferred over powertop.service for the following reasons:

  • The powertop2tuned utility represents integration of PowerTOP into Tuned, which enables to benefit of advantages of both tools.
  • The powertop2tuned utility allows for fine-grained control of enabled tuning.
  • With powertop2tuned, potentially dangerous tuning are not automatically enabled.
  • With powertop2tuned, rollback is possible without reboot.

Chapter 11. Getting started with perf

As a system administrator, you can use the perf tool to collect and analyze performance data of your system.

11.1. Introduction to perf

The perf user-space tool interfaces with the kernel-based subsystem Performance Counters for Linux (PCL). perf is a powerful tool that uses the Performance Monitoring Unit (PMU) to measure, record, and monitor a variety of hardware and software events. perf also supports tracepoints, kprobes, and uprobes.

11.2. Installing perf

This procedure installs the perf user-space tool.

Procedure

  • Install the perf tool:

    # yum install perf

11.3. Common Perf Commands

This section provides an overview of commonly used perf commands.

Commonly used perf commands

perf stat
This command provides overall statistics for common performance events, including instructions executed and clock cycles consumed. Options allow for selection of events other than the default measurement events.
perf record
This command records performance data into a file, perf.data, which can be later analyzed using the perf report command.
perf report
This command reads and displays the performance data from the perf.data file created by perf record.
perf list
This command lists the events available on a particular machine. These events will vary based on performance monitoring hardware and software configuration of the system.
perf top
This command performs a similar function to the top utility. It generates and displays a performance counter profile in realtime.
perf trace
This command performs a similar function to the strace tool. It monitors the system calls used by a specified thread or process and all signals received by that application.
perf help
This command displays a complete list of perf commands.

Additional resources

  • To list additional subcommand options of the subcommands and their descriptions, add the -h option to the target command.

11.4. Real time profiling of CPU usage with perf top

You can use the perf top command to measure CPU usage of different functions in real time.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.

11.4.1. The purpose of perf top

The perf top command is used for real time system profiling and functions similarly to the top utility. However, where the top utility generally shows you how much CPU time a given process or thread is using, perf top shows you how much CPU time each specific function uses. In its default state, perf top tells you about functions being used across all CPUs in both the user-space and the kernel-space. To use perf top you need root access.

11.4.2. Profiling CPU usage with perf top

This procedure activates perf top and profiles CPU usage in real time.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.
  • You have root access

Procedure

  • Start the perf top monitoring interface:

    # perf top

    Example 11.1. Perf top output

    --------------------------------------------------------------------
    PerfTop:   20806 irqs/sec  kernel:57.3%  exact: 100.0% lost: 0/0 drop: 0/0 [4000Hz cycles],  (all, 8 CPUs)
    ---------------------------------------------------------------------
    Overhead  Shared Object       Symbol
       2.20%  [kernel]            [k] do_syscall_64
       2.17%  [kernel]            [k] module_get_kallsym
       1.49%  [kernel]            [k] copy_user_enhanced_fast_string
       1.37%  libpthread-2.29.so  [.] __pthread_mutex_lock
       1.31%  [unknown]           [.] 0000000000000000
       1.07%  [kernel]            [k] psi_task_change
       1.04%  [kernel]            [k] switch_mm_irqs_off
       0.94%  [kernel]            [k] __fget
       0.74%  [kernel]            [k] entry_SYSCALL_64
       0.69%  [kernel]            [k] syscall_return_via_sysret
       0.69%  libxul.so           [.] 0x000000000113f9b0
       0.67%  [kernel]            [k] kallsyms_expand_symbol.constprop.0
       0.65%  firefox             [.] moz_xmalloc
       0.65%  libpthread-2.29.so  [.] __pthread_mutex_unlock_usercnt
       0.60%  firefox             [.] free
       0.60%  libxul.so           [.] 0x000000000241d1cd
       0.60%  [kernel]            [k] do_sys_poll
       0.58%  [kernel]            [k] menu_select
       0.56%  [kernel]            [k] _raw_spin_lock_irqsave
       0.55%  perf                [.] 0x00000000002ae0f3

    In the previous example, the kernel function do_syscall_64 is using the most CPU time.

Additional resources

  • The perf-top(1) man page.

11.4.3. Interpretation of perf top output

The "Overhead" column
Displays the percent of CPU a given function is using.
The "Shared Object" column
Displays name of the program or library which is using the function.
The "Symbol" column
Displays the function name or symbol. Functions executed in the kernel-space are identified by [k] and functions executed in the user-space are identified by [.].

11.4.4. Why perf displays some function names as raw function addresses

For kernel functions, perf uses the information from the /proc/kallsyms file to map the samples to their respective function names or symbols. For functions executed in the user space, however, you might see raw function addresses because the binary is stripped.

The debuginfo package of the executable must be installed or, if the executable is a locally developed application, the application must be compiled with debugging information turned on (the -g option in GCC) to display the function names or symbols in such a situation.

11.4.5. Enabling debug and source repositories

A standard installation of Red Hat Enterprise Linux does not enable the debug and source repositories. These repositories contain information needed to debug the system components and measure their performance.

Procedure

  • Enable the source and debug information package channels:

    # subscription-manager repos --enable rhel-8-for-$(uname -i)-baseos-debug-rpms
    # subscription-manager repos --enable rhel-8-for-$(uname -i)-baseos-source-rpms
    # subscription-manager repos --enable rhel-8-for-$(uname -i)-appstream-debug-rpms
    # subscription-manager repos --enable rhel-8-for-$(uname -i)-appstream-source-rpms

    The $(uname -i) part is automatically replaced with a matching value for architecture of your system:

    Architecture nameValue

    64-bit Intel and AMD

    x86_64

    64-bit ARM

    aarch64

    IBM POWER

    ppc64le

    IBM Z

    s390x

11.4.6. Getting debuginfo packages for an application or library using GDB

Debugging information is required to debug code. For code that is installed from a package, the GNU Debugger (GDB) automatically recognizes missing debug information, resolves the package name and provides concrete advice on how to get the package.

Prerequisites

  • The application or library you want to debug must be installed on the system.
  • GDB and the debuginfo-install tool must be installed on the system. For details, see Setting up to debug applications.
  • Channels providing debuginfo and debugsource packages must be configured and enabled on the system.

Procedure

  1. Start GDB attached to the application or library you want to debug. GDB automatically recognizes missing debugging information and suggests a command to run.

    $ gdb -q /bin/ls
    Reading symbols from /bin/ls...Reading symbols from .gnu_debugdata for /usr/bin/ls...(no debugging symbols found)...done.
    (no debugging symbols found)...done.
    Missing separate debuginfos, use: dnf debuginfo-install coreutils-8.30-6.el8.x86_64
    (gdb)
  2. Exit GDB: type q and confirm with Enter.

    (gdb) q
  3. Run the command suggested by GDB to install the required debuginfo packages:

    # dnf debuginfo-install coreutils-8.30-6.el8.x86_64

    The dnf package management tool provides a summary of the changes, asks for confirmation and once you confirm, downloads and installs all the necessary files.

  4. In case GDB is not able to suggest the debuginfo package, follow the procedure described in Getting debuginfo packages for an application or library manually.

11.5. Counting events during process execution

You can use the perf stat command to count hardware and software events during process execution.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.

11.5.1. The purpose of perf stat

The perf stat command executes a specified command, keeps a running count of hardware and software event occurrences during the commands execution, and generates statistics of these counts. If you do not specify any events, then perf stat counts a set of common hardware and software events.

11.5.2. Counting events with perf stat

You can use perf stat to count hardware and software event occurrences during command execution and generate statistics of these counts. By default, perf stat operates in per-thread mode.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.

Procedure

  • Count the events.

    • Running the perf stat command without root access will only count events occurring in the user space:

      $ perf stat ls

      Example 11.2. Output of perf stat ran without root access

      Desktop  Documents  Downloads  Music  Pictures  Public  Templates  Videos
      
       Performance counter stats for 'ls':
      
                    1.28 msec task-clock:u               #    0.165 CPUs utilized
                       0      context-switches:u         #    0.000 M/sec
                       0      cpu-migrations:u           #    0.000 K/sec
                     104      page-faults:u              #    0.081 M/sec
               1,054,302      cycles:u                   #    0.823 GHz
               1,136,989      instructions:u             #    1.08  insn per cycle
                 228,531      branches:u                 #  178.447 M/sec
                  11,331      branch-misses:u            #    4.96% of all branches
      
             0.007754312 seconds time elapsed
      
             0.000000000 seconds user
             0.007717000 seconds sys

      As you can see in the previous example, when perf stat runs without root access the event names are followed by :u, indicating that these events were counted only in the user-space.

    • To count both user-space and kernel-space events, you must have root access when running perf stat:

      # perf stat ls

      Example 11.3. Output of perf stat ran with root access

      Desktop  Documents  Downloads  Music  Pictures  Public  Templates  Videos
      
       Performance counter stats for 'ls':
      
                    3.09 msec task-clock                #    0.119 CPUs utilized
                      18      context-switches          #    0.006 M/sec
                       3      cpu-migrations            #    0.969 K/sec
                     108      page-faults               #    0.035 M/sec
               6,576,004      cycles                    #    2.125 GHz
               5,694,223      instructions              #    0.87  insn per cycle
               1,092,372      branches                  #  352.960 M/sec
                  31,515      branch-misses             #    2.89% of all branches
      
             0.026020043 seconds time elapsed
      
             0.000000000 seconds user
             0.014061000 seconds sys
      • By default, perf stat operates in per-thread mode. To change to CPU-wide event counting, pass the -a option to perf stat. To count CPU-wide events, you need root access:

        # perf stat -a ls

Additional resources

  • The perf-stat(1) man page.

11.5.3. Interpretation of perf stat output

perf stat executes a specified command and counts event occurrences during the commands execution and displays statistics of these counts in three columns:

  1. The number of occurrences counted for a given event
  2. The name of the event that was counted
  3. When related metrics are available, a ratio or percentage is displayed after the hash sign (#) in the right-most column.

    • For example, when running in default mode, perf stat counts both cycles and instructions and, therefore, calculates and displays instructions per cycle in the right-most column. You can see similar behavior with regard to branch-misses as a percent of all branches since both events are counted by default.

11.5.4. Attaching perf stat to a running process

You can attach perf stat to a running process. This will instruct perf stat to count event occurrences only in the specified processes during the execution of a command.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.

Procedure

  • Attach perf stat to a running process:

    $ perf stat -p ID1,ID2 sleep seconds

    The previous example counts events in the processes with the IDs of ID1 and ID2 for a time period of seconds seconds as dictated by using the sleep command.

Additional resources

  • The perf-stat(1) man page.

11.6. Recording and analyzing performance profiles with perf

The perf tool allows you to record performance data and analyze it at a later time.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.

11.6.1. The purpose of perf record

The perf record command samples performance data and stores it in a file, perf.data, which can be read and visualized with other perf commands. perf.data is generated in the current directory and can be accessed at a later time, possibly on a different machine.

If you do not specify a command for perf record to record during, it will record until you manually stop the process by pressing Ctrl+C. You can attach perf record to specific processes by passing the -p option followed by one or more process IDs. You can run perf record without root access, however, doing so will only sample performance data in the user space. In the default mode, perf record uses CPU cycles as the sampling event and operates in per-thread mode with inherit mode enabled.

11.6.2. Recording a performance profile without root access

You can use perf record without root access to sample and record performance data in the user-space only.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.

Procedure

  • Sample and record the performance data:

    $ perf record command

    Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.

Additonal resources

  • The perf-record(1) man page.

11.6.3. Recording a performance profile with root access

You can use perf record with root access to sample and record performance data in both the user-space and the kernel-space simultaneously.

Prerequisites

  • You have the perf user space tool installed as described in Installing perf.
  • You have root access.

Procedure

  • Sample and record the performance data:

    # perf record command

    Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.

Additonal resources

  • The perf-record(1) man page.

11.6.4. Recording a performance profile in per-CPU mode

You can use perf record in per-CPU mode to sample and record performance data in both and user-space and the kernel-space simultaneously across all threads on a monitored CPU. By default, per-CPU mode monitors all online CPUs.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Sample and record the performance data:

    # perf record -a command

    Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.

Additional resources

  • The perf-record(1) man page.

11.6.5. Capturing call graph data with perf record

You can configure the perf record tool so that it records which function is calling other functions in the performance profile. This helps to identify a bottleneck if several processes are calling the same function.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Sample and record performance data with the --call-graph option:

    $ perf record --call-graph method command
    • Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.
    • Replace method with one of the following unwinding methods:

      fp
      Uses the frame pointer method. Depending on compiler optimization, such as with binaries built with the GCC option --fomit-frame-pointer, this may not be able to unwind the stack.
      dwarf
      Uses DWARF Call Frame Information to unwind the stack.
      lbr
      Uses the last branch record hardware on Intel processors.

Additional resources

  • The perf-record(1) man page.

11.6.6. Analyzing perf.data with perf report

You can use perf report to display and analyze a perf.data file.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.
  • There is a perf.data file in the current directory.
  • If the perf.data file was created with root access, you need to run perf report with root access too.

Procedure

  • Display the contents of the perf.data file for further analysis:

    # perf report

    Example 11.4. Example output

    Samples: 2K of event 'cycles', Event count (approx.): 235462960
    Overhead  Command          Shared Object                     Symbol
       2.36%  kswapd0          [kernel.kallsyms]                 [k] page_vma_mapped_walk
       2.13%  sssd_kcm         libc-2.28.so                      [.] __memset_avx2_erms
       2.13%  perf             [kernel.kallsyms]                 [k] smp_call_function_single
       1.53%  gnome-shell      libc-2.28.so                      [.] __strcmp_avx2
       1.17%  gnome-shell      libglib-2.0.so.0.5600.4           [.] g_hash_table_lookup
       0.93%  Xorg             libc-2.28.so                      [.] __memmove_avx_unaligned_erms
       0.89%  gnome-shell      libgobject-2.0.so.0.5600.4        [.] g_object_unref
       0.87%  kswapd0          [kernel.kallsyms]                 [k] page_referenced_one
       0.86%  gnome-shell      libc-2.28.so                      [.] __memmove_avx_unaligned_erms
       0.83%  Xorg             [kernel.kallsyms]                 [k] alloc_vmap_area
       0.63%  gnome-shell      libglib-2.0.so.0.5600.4           [.] g_slice_alloc
       0.53%  gnome-shell      libgirepository-1.0.so.1.0.0      [.] g_base_info_unref
       0.53%  gnome-shell      ld-2.28.so                        [.] _dl_find_dso_for_object
       0.49%  kswapd0          [kernel.kallsyms]                 [k] vma_interval_tree_iter_next
       0.48%  gnome-shell      libpthread-2.28.so                [.] __pthread_getspecific
       0.47%  gnome-shell      libgirepository-1.0.so.1.0.0      [.] 0x0000000000013b1d
       0.45%  gnome-shell      libglib-2.0.so.0.5600.4           [.] g_slice_free1
       0.45%  gnome-shell      libgobject-2.0.so.0.5600.4        [.] g_type_check_instance_is_fundamentally_a
       0.44%  gnome-shell      libc-2.28.so                      [.] malloc
       0.41%  swapper          [kernel.kallsyms]                 [k] apic_timer_interrupt
       0.40%  gnome-shell      ld-2.28.so                        [.] _dl_lookup_symbol_x
       0.39%  kswapd0          [kernel.kallsyms]                 [k] __raw_callee_save___pv_queued_spin_unlock

Additonal resources

  • The perf-report(1) man page.

11.6.7. Interpretation of perf report output

The table displayed by running the perf report command sorts the data into several columns:

The 'Overhead' column
Indicates what percentage of overall samples were collected in that particular function.
The 'Command' column
Tells you which process the samples were collected from.
The 'Shared Object' column
Displays the name of the ELF image where the samples come from (the name [kernel.kallsyms] is used when the samples come from the kernel).
The 'Symbol' column
Displays the function name or symbol.

In default mode, the functions are sorted in descending order with those with the highest overhead displayed first.

11.6.8. Why perf displays some function names as raw function addresses

For kernel functions, perf uses the information from the /proc/kallsyms file to map the samples to their respective function names or symbols. For functions executed in the user space, however, you might see raw function addresses because the binary is stripped.

The debuginfo package of the executable must be installed or, if the executable is a locally developed application, the application must be compiled with debugging information turned on (the -g option in GCC) to display the function names or symbols in such a situation.

Note

It is not necessary to re-run perf record after installing the debuginfo associated with an executable. Simply re-run perf report.

11.6.9. Enabling debug and source repositories

A standard installation of Red Hat Enterprise Linux does not enable the debug and source repositories. These repositories contain information needed to debug the system components and measure their performance.

Procedure

  • Enable the source and debug information package channels:

    # subscription-manager repos --enable rhel-8-for-$(uname -i)-baseos-debug-rpms
    # subscription-manager repos --enable rhel-8-for-$(uname -i)-baseos-source-rpms
    # subscription-manager repos --enable rhel-8-for-$(uname -i)-appstream-debug-rpms
    # subscription-manager repos --enable rhel-8-for-$(uname -i)-appstream-source-rpms

    The $(uname -i) part is automatically replaced with a matching value for architecture of your system:

    Architecture nameValue

    64-bit Intel and AMD

    x86_64

    64-bit ARM

    aarch64

    IBM POWER

    ppc64le

    IBM Z

    s390x

11.6.10. Getting debuginfo packages for an application or library using GDB

Debugging information is required to debug code. For code that is installed from a package, the GNU Debugger (GDB) automatically recognizes missing debug information, resolves the package name and provides concrete advice on how to get the package.

Prerequisites

  • The application or library you want to debug must be installed on the system.
  • GDB and the debuginfo-install tool must be installed on the system. For details, see Setting up to debug applications.
  • Channels providing debuginfo and debugsource packages must be configured and enabled on the system.

Procedure

  1. Start GDB attached to the application or library you want to debug. GDB automatically recognizes missing debugging information and suggests a command to run.

    $ gdb -q /bin/ls
    Reading symbols from /bin/ls...Reading symbols from .gnu_debugdata for /usr/bin/ls...(no debugging symbols found)...done.
    (no debugging symbols found)...done.
    Missing separate debuginfos, use: dnf debuginfo-install coreutils-8.30-6.el8.x86_64
    (gdb)
  2. Exit GDB: type q and confirm with Enter.

    (gdb) q
  3. Run the command suggested by GDB to install the required debuginfo packages:

    # dnf debuginfo-install coreutils-8.30-6.el8.x86_64

    The dnf package management tool provides a summary of the changes, asks for confirmation and once you confirm, downloads and installs all the necessary files.

  4. In case GDB is not able to suggest the debuginfo package, follow the procedure described in Getting debuginfo packages for an application or library manually.

Chapter 12. Monitoring system performance with perf

As a system administrator you can use the perf tool to collect and analyze performance data of your system.

12.1. Recording a performance profile in per-CPU mode

You can use perf record in per-CPU mode to sample and record performance data in both and user-space and the kernel-space simultaneously across all threads on a monitored CPU. By default, per-CPU mode monitors all online CPUs.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Sample and record the performance data:

    # perf record -a command

    Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.

Additional resources

  • The perf-record(1) man page.

12.2. Capturing call graph data with perf record

You can configure the perf record tool so that it records which function is calling other functions in the performance profile. This helps to identify a bottleneck if several processes are calling the same function.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Sample and record performance data with the --call-graph option:

    $ perf record --call-graph method command
    • Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.
    • Replace method with one of the following unwinding methods:

      fp
      Uses the frame pointer method. Depending on compiler optimization, such as with binaries built with the GCC option --fomit-frame-pointer, this may not be able to unwind the stack.
      dwarf
      Uses DWARF Call Frame Information to unwind the stack.
      lbr
      Uses the last branch record hardware on Intel processors.

Additional resources

  • The perf-record(1) man page.

12.3. Identifying busy CPUs with perf

When investigating performance issues on a system, you can use the perf tool to identify the busiest CPUs in order to focus your efforts.

12.3.1. Displaying which CPU events were counted on with perf stat

You can use perf stat to display which CPU events were counted on by disabling CPU count aggregation. You must count events in system-wide mode by using the -a flag in order to use this functionality.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Count the events with CPU count aggregation disabled:

    # perf stat -a -A sleep seconds

    The previous example displays counts of a default set of common hardware and software events recorded over a time period of seconds seconds, as dictated by using the sleep command, over each individual CPU in ascending order, starting with CPU0. As such, it may be useful to specify an event such as cycles:

    # perf stat -a -A -e cycles sleep seconds

12.3.2. Displaying which CPU samples were taken on with perf report

The perf record command samples performance data and stores this data in a perf.data file which can be read with the perf report command. The perf record command always records which CPU samples were taken on. You can configure perf report to display this information.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.
  • There is a perf.data file created with perf record in the current directory. If the perf.data file was created with root access, you need to run perf report with root access too.

Procedure

  • Display the contents of the perf.data file for further analysis while sorting by CPU:

    # perf report --sort cpu
    • You can sort by CPU and command to display more detailed information about where CPU time is being spent:

      # perf report --sort cpu,comm

      This example will list commands from all monitored CPUs by total overhead in descending order of overhead usage and identify the CPU the command was executed on.

Additional resources

12.3.3. Displaying specific CPUs during profiling with perf top

You can configure perf top to display specific CPUs and their relative usage while profiling your system in real time.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Start the perf top interface while sorting by CPU:

    # perf top --sort cpu

    This example will list CPUs and their respective overhead in descending order of overhead usage in real time.

    • You can sort by CPU and command for more detailed information of where CPU time is being spent:

      # perf top --sort cpu,comm

      This example will list commands by total overhead in descending order of overhead usage and identify the CPU the command was executed on in real time.

12.4. Monitoring specific CPUs with perf

You can configure the perf tool to monitor only specific CPUs of interest.

12.4.1. Monitoring specific CPUs with perf record and perf report

You can configure perf record to only sample specific CPUs of interest and analyze the generated perf.data file with perf report for further analysis.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  1. Sample and record the performance data in the specific CPU’s, generating a perf.data file:

    • Using a comma separated list of CPUs:

      # perf record -C 0,1 sleep seconds

      The previous example samples and records data in CPUs 0 and 1 for a period of seconds seconds as dictated by the use of the sleep command.

    • Using a range of CPUs:

      # perf record -C 0-2 sleep seconds

      The previous example samples and records data in all CPUs from CPU 0 to 2 for a period of seconds seconds as dictated by the use of the sleep command.

  2. Display the contents of the perf.data file for further analysis:

    # perf report

    This example will display the contents of perf.data. If you are monitoring several CPUs and want to know which CPU data was sampled on, see Displaying which CPU samples were taken on with perf report.

12.4.2. Displaying specific CPUs during profiling with perf top

You can configure perf top to display specific CPUs and their relative usage while profiling your system in real time.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Start the perf top interface while sorting by CPU:

    # perf top --sort cpu

    This example will list CPUs and their respective overhead in descending order of overhead usage in real time.

    • You can sort by CPU and command for more detailed information of where CPU time is being spent:

      # perf top --sort cpu,comm

      This example will list commands by total overhead in descending order of overhead usage and identify the CPU the command was executed on in real time.

12.5. Generating a perf.data file that is readable on a different device

You can use the perf tool to record performance data into a perf.data file to be analyzed on a different device.

Prerequisites

Procedure

  1. Capture performance data you are interested in investigating further:

    # perf record -a --call-graph fp sleep seconds

    This example would generate a perf.data over the entire system for a period of seconds seconds as dictated by the use of the sleep command. It would also capture call graph data using the frame pointer method.

  2. Generate an archive file containing debug symbols of the recorded data:

    # perf archive

Verification steps

  • Verify that the archive file has been generated in your current active directory:

    # ls perf.data*

    The output will display every file in your current directory that begins with perf.data. The archive file will be named either:

    perf.data.tar.gz

    or

    perf data.tar.bz2

Additional resources

12.6. Analyzing a perf.data file that was created on a different device

You can use the perf tool to analyze a perf.data file that was generated on a different device.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.
  • A perf.data file and associated archive file generated on a different device are present on the current device being used.

Procedure

  1. Copy both the perf.data file and the archive file into your current active directory.
  2. Extract the archive file into ~/.debug:

    # mkdir -p ~/.debug
    # tar xf perf.data.tar.bz2 -C ~/.debug
    Note

    The archive file might also be named perf.data.tar.gz.

  3. Open the perf.data file for further analysis:

    # perf report

Chapter 13. Monitoring application performance with perf

This section describes how to use the perf tool to monitor application performance.

13.1. Attaching perf record to a running process

Prerequisites

You can attach perf record to a running process. This will instruct perf record to only sample and record performance data in the specified processes.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Attach perf record to a running process:

    $ perf record -p ID1,ID2 sleep seconds

    The previous example samples and records performance data of the processes with the process ID’s ID1 and ID2 for a time period of seconds seconds as dictated by using the sleep command. You can also configure perf to record events in specific threads:

    $ perf record -t ID1,ID2 sleep seconds
    Note

    When using the -t flag and stipulating thread ID’s, perf disables inheritance by default. You can enable inheritance by adding the --inherit option.

13.2. Capturing call graph data with perf record

You can configure the perf record tool so that it records which function is calling other functions in the performance profile. This helps to identify a bottleneck if several processes are calling the same function.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.

Procedure

  • Sample and record performance data with the --call-graph option:

    $ perf record --call-graph method command
    • Replace command with the command you want to sample data during. If you do not specify a command, then perf record will sample data until you manually stop it by pressing Ctrl+C.
    • Replace method with one of the following unwinding methods:

      fp
      Uses the frame pointer method. Depending on compiler optimization, such as with binaries built with the GCC option --fomit-frame-pointer, this may not be able to unwind the stack.
      dwarf
      Uses DWARF Call Frame Information to unwind the stack.
      lbr
      Uses the last branch record hardware on Intel processors.

Additional resources

  • The perf-record(1) man page.

13.3. Analyzing perf.data with perf report

You can use perf report to display and analyze a perf.data file.

Prerequisites

  • The perf user space tool is installed. For more information, see Installing perf.
  • There is a perf.data file in the current directory.
  • If the perf.data file was created with root access, you need to run perf report with root access too.

Procedure

  • Display the contents of the perf.data file for further analysis:

    # perf report

    Example 13.1. Example output

    Samples: 2K of event 'cycles', Event count (approx.): 235462960
    Overhead  Command          Shared Object                     Symbol
       2.36%  kswapd0          [kernel.kallsyms]                 [k] page_vma_mapped_walk
       2.13%  sssd_kcm         libc-2.28.so                      [.] __memset_avx2_erms
       2.13%  perf             [kernel.kallsyms]                 [k] smp_call_function_single
       1.53%  gnome-shell      libc-2.28.so                      [.] __strcmp_avx2
       1.17%  gnome-shell      libglib-2.0.so.0.5600.4           [.] g_hash_table_lookup
       0.93%  Xorg             libc-2.28.so                      [.] __memmove_avx_unaligned_erms
       0.89%  gnome-shell      libgobject-2.0.so.0.5600.4        [.] g_object_unref
       0.87%  kswapd0          [kernel.kallsyms]                 [k] page_referenced_one
       0.86%  gnome-shell      libc-2.28.so                      [.] __memmove_avx_unaligned_erms
       0.83%  Xorg             [kernel.kallsyms]                 [k] alloc_vmap_area
       0.63%  gnome-shell      libglib-2.0.so.0.5600.4           [.] g_slice_alloc
       0.53%  gnome-shell      libgirepository-1.0.so.1.0.0      [.] g_base_info_unref
       0.53%  gnome-shell      ld-2.28.so                        [.] _dl_find_dso_for_object
       0.49%  kswapd0          [kernel.kallsyms]                 [k] vma_interval_tree_iter_next
       0.48%  gnome-shell      libpthread-2.28.so                [.] __pthread_getspecific
       0.47%  gnome-shell      libgirepository-1.0.so.1.0.0      [.] 0x0000000000013b1d
       0.45%  gnome-shell      libglib-2.0.so.0.5600.4           [.] g_slice_free1
       0.45%  gnome-shell      libgobject-2.0.so.0.5600.4        [.] g_type_check_instance_is_fundamentally_a
       0.44%  gnome-shell      libc-2.28.so                      [.] malloc
       0.41%  swapper          [kernel.kallsyms]                 [k] apic_timer_interrupt
       0.40%  gnome-shell      ld-2.28.so                        [.] _dl_lookup_symbol_x
       0.39%  kswapd0          [kernel.kallsyms]                 [k] __raw_callee_save___pv_queued_spin_unlock

Additonal resources

  • The perf-report(1) man page.

Chapter 14. Configuring an operating system to optimize CPU utilization

This section describes how to configure the operating system to optimize CPU utilization across their workloads.

14.1. Tools for monitoring and diagnosing processor issues

The following are the tools available in Red Hat Enterprise Linux 8 to monitor and diagnose processor-related performance issues:

  • turbostat tool prints counter results at specified intervals to help administrators identify unexpected behavior in servers, such as excessive power usage, failure to enter deep sleep states, or system management interrupts (SMIs) being created unnecessarily.
  • numactl utility provides a number of options to manage processor and memory affinity. The numactl package includes the libnuma library which offers a simple programming interface to the NUMA policy supported by the kernel, and can be used for more fine-grained tuning than the numactl application.
  • numastat tool displays per-NUMA node memory statistics for the operating system and its processes, and shows administrators whether the process memory is spread throughout a system or is centralized on specific nodes. This tool is provided by the numactl package.
  • numad is an automatic NUMA affinity management daemon. It monitors NUMA topology and resource usage within a system in order to dynamically improve NUMA resource allocation and management.
  • /proc/interrupts file displays the interrupt request (IRQ) number, the number of similar interrupt requests handled by each processor in the system, the type of interrupt sent, and a comma-separated list of devices that respond to the listed interrupt request.
  • pqos utility is available in the intel-cmt-cat package. It monitors CPU cache and memory bandwidth on recent Intel processors. It monitors:

    • The instructions per cycle (IPC).
    • The count of last level cache MISSES.
    • The size in kilobytes that the program executing in a given CPU occupies in the LLC.
    • The bandwidth to local memory (MBL).
    • The bandwidth to remote memory (MBR).
  • x86_energy_perf_policy tool allows administrators to define the relative importance of performance and energy efficiency. This information can then be used to influence processors that support this feature when they select options that trade off between performance and energy efficiency.
  • taskset tool is provided by the util-linux package. It allows administrators to retrieve and set the processor affinity of a running process, or launch a process with a specified processor affinity.

Additional resources

  • For more information, see the man pages of turbostat, numactl, numastat, numa, numad, pqos, x86_energy_perf_policy, and taskset.

14.2. Determining system topology

In modern computing, the idea of a CPU is a misleading one, as most modern systems have multiple processors. The topology of the system is the way these processors are connected to each other and to other system resources. This can affect system and application performance, and the tuning considerations for a system.

14.2.1. Types of system topology

The following are the two primary types of topology used in modern computing:

Symmetric Multi-Processor (SMP) topology
SMP topology allows all processors to access memory in the same amount of time. However, because shared and equal memory access inherently forces serialized memory accesses from all the CPUs, SMP system scaling constraints are now generally viewed as unacceptable. For this reason, practically all modern server systems are NUMA machines.
Non-Uniform Memory Access (NUMA) topology

NUMA topology was developed more recently than SMP topology. In a NUMA system, multiple processors are physically grouped on a socket. Each socket has a dedicated area of memory and processors that have local access to that memory, these are referred to collectively as a node. Processors on the same node have high speed access to that node’s memory bank, and slower access to memory banks not on their node.

Therefore, there is a performance penalty when accessing non-local memory. Thus, performance sensitive applications on a system with NUMA topology should access memory that is on the same node as the processor executing the application, and should avoid accessing remote memory wherever possible.

Multi-threaded applications that are sensitive to performance may benefit from being configured to execute on a specific NUMA node rather than a specific processor. Whether this is suitable depends on your system and the requirements of your application. If multiple application threads access the same cached data, then configuring those threads to execute on the same processor may be suitable. However, if multiple threads that access and cache different data execute on the same processor, each thread may evict cached data accessed by a previous thread. This means that each thread 'misses' the cache and wastes execution time fetching data from memory and replacing it in the cache. Use the perf tool to check for an excessive number of cache misses.

14.2.2. Displaying system topologies

There are a number of commands that help understand the topology of a system. This procedure describes how to determine the system topology.

Procedure

  • To display an overview of your system topology:

    $ numactl --hardware
    available: 4 nodes (0-3)
    node 0 cpus: 0 4 8 12 16 20 24 28 32 36
    node 0 size: 65415 MB
    node 0 free: 43971 MB
    [...]
  • To gather the information about the CPU architecture, such as the number of CPUs, threads, cores, sockets, and NUMA nodes:

    $ lscpu
    Architecture:          x86_64
    CPU op-mode(s):        32-bit, 64-bit
    Byte Order:            Little Endian
    CPU(s):                40
    On-line CPU(s) list:   0-39
    Thread(s) per core:    1
    Core(s) per socket:    10
    Socket(s):             4
    NUMA node(s):          4
    Vendor ID:             GenuineIntel
    CPU family:            6
    Model:                 47
    Model name:            Intel(R) Xeon(R) CPU E7- 4870  @ 2.40GHz
    Stepping:              2
    CPU MHz:               2394.204
    BogoMIPS:              4787.85
    Virtualization:        VT-x
    L1d cache:             32K
    L1i cache:             32K
    L2 cache:              256K
    L3 cache:              30720K
    NUMA node0 CPU(s):     0,4,8,12,16,20,24,28,32,36
    NUMA node1 CPU(s):     2,6,10,14,18,22,26,30,34,38
    NUMA node2 CPU(s):     1,5,9,13,17,21,25,29,33,37
    NUMA node3 CPU(s):     3,7,11,15,19,23,27,31,35,39
  • To view a graphical representation of your system:

    # yum install hwloc-gui
    # lstopo

    Figure 14.1. The lstopo output

    lstopo
  • To view the detailed textual output:

    # yum install hwloc
    # lstopo-no-graphics
    Machine (15GB)
      Package L#0 + L3 L#0 (8192KB)
        L2 L#0 (256KB) + L1d L#0 (32KB) + L1i L#0 (32KB) + Core L#0
            PU L#0 (P#0)
            PU L#1 (P#4)
           HostBridge L#0
        PCI 8086:5917
            GPU L#0 "renderD128"
            GPU L#1 "controlD64"
            GPU L#2 "card0"
        PCIBridge
            PCI 8086:24fd
              Net L#3 "wlp61s0"
        PCIBridge
            PCI 8086:f1a6
        PCI 8086:15d7
            Net L#4 "enp0s31f6"

Additional resources

  • For more information, see the numactl, lscpu, and lstopo man pages.

14.3. Tuning scheduling policy

In Red Hat Enterprise Linux, the smallest unit of process execution is called a thread. The system scheduler determines which processor runs a thread, and for how long the thread runs. However, because the scheduler’s primary concern is to keep the system busy, it may not schedule threads optimally for application performance.

For example, say an application on a NUMA system is running on Node A when a processor on Node B becomes available. To keep the processor on Node B busy, the scheduler moves one of the application’s threads to Node B. However, the application thread still requires access to memory on Node A. But, this memory will take longer to access because the thread is now running on Node B and Node A memory is no longer local to the thread. Thus, it may take longer for the thread to finish running on Node B than it would have taken to wait for a processor on Node A to become available, and then to execute the thread on the original node with local memory access.

Performance sensitive applications often benefit from the designer or administrator determining where threads are run. The Linux scheduler implements a number of scheduling policies which determine where and for how long a thread runs. The following are the two major categories of scheduling policies:

14.3.1. Static priority scheduling with SCHED_FIFO

The SCHED_FIFO, also called static priority scheduling, is a realtime policy that defines a fixed priority for each thread. This policy allows administrators to improve event response time and reduce latency. It is recommended to not execute this policy for an extended period of time for time sensitive tasks.

When SCHED_FIFO is in use, the scheduler scans the list of all the SCHED_FIFO threads in order of priority and schedules the highest priority thread that is ready to run. The priority level of a SCHED_FIFO thread can be any integer from 1 to 99, where 99 is treated as the highest priority. Red Hat recommends starting with a lower number and increasing priority only when you identify latency issues.

Warning

Because realtime threads are not subject to time slicing, Red Hat does not recommend setting a priority as 99. This keeps your process at the same priority level as migration and watchdog threads; if your thread goes into a computational loop and these threads are blocked, they will not be able to run. Systems with a single processor will eventually hang in this situation.

Administrators can limit SCHED_FIFO bandwidth to prevent realtime application programmers from initiating realtime tasks that monopolize the processor.

The following are some of the parameters used in this policy:

/proc/sys/kernel/sched_rt_period_us
This parameter defines the time period, in microseconds, that is considered to be one hundred percent of the processor bandwidth. The default value is 1000000 μs, or 1 second.
/proc/sys/kernel/sched_rt_runtime_us
This parameter defines the time period, in microseconds, that is devoted to running real-time threads. The default value is 950000 μs, or 0.95 seconds.

14.3.2. Round robin priority scheduling with SCHED_RR

The SCHED_RR is a round-robin variant of the SCHED_FIFO. This policy is useful when multiple threads need to run at the same priority level.

Like SCHED_FIFO, SCHED_RR is a realtime policy that defines a fixed priority for each thread. The scheduler scans the list of all SCHED_RR threads in order of priority and schedules the highest priority thread that is ready to run. However, unlike SCHED_FIFO, threads that have the same priority are scheduled in a round-robin style within a certain time slice.

You can set the value of this time slice in milliseconds with the sched_rr_timeslice_ms kernel parameter in the /proc/sys/kernel/sched_rr_timeslice_ms file. The lowest value is 1 millisecond.

14.3.3. Normal scheduling with SCHED_OTHER

The SCHED_OTHER is the default scheduling policy in Red Hat Enterprise Linux 8. This policy uses the Completely Fair Scheduler (CFS) to allow fair processor access to all threads scheduled with this policy. This policy is most useful when there are a large number of threads or when data throughput is a priority, as it allows more efficient scheduling of threads over time.

When this policy is in use, the scheduler creates a dynamic priority list based partly on the niceness value of each process thread. Administrators can change the niceness value of a process, but cannot change the scheduler’s dynamic priority list directly.

14.3.4. Setting scheduler policies

Check and adjust scheduler policies and priorities by using the chrt command line tool. It can start new processes with the desired properties, or change the properties of a running process. It can also be used for setting the policy at runtime.

Procedure

  1. View the process ID (PID) of the active processes:

    # ps

    Use the --pid or -p option with the ps command to view the details of the particular PID.

  2. Check the scheduling policy, PID, and priority of a particular process:

    # chrt -p 468
    pid 468's current scheduling policy: SCHED_FIFO
    pid 468's current scheduling priority: 85
    
    # chrt -p 476
    pid 476's current scheduling policy: SCHED_OTHER
    pid 476's current scheduling priority: 0

    Here, 468 and 476 are PID of a process.

  3. Set the scheduling policy of a process:

    1. For example, to set the process with PID 1000 to SCHED_FIFO, with a priority of 50:

      # chrt -f -p 50 1000
    2. For example, to set the process with PID 1000 to SCHED_OTHER, with a priority of 0:

      # chrt -o -p 0 1000
    3. For example, to set the process with PID 1000 to SCHED_RR, with a priority of 10:

      # chrt -r -p 10 1000
    4. To start a new application with a particular policy and priority, specify the name of the application:

      # chrt -f 36 /bin/my-app

Additional resources

14.3.5. Policy options for the chrt command

To set the scheduling policy of a process, use the appropriate command option:

Table 14.1. Policy Options for the chrt Command

Short optionLong optionDescription

-f

--fifo

Set schedule to SCHED_FIFO

-o

--other

Set schedule to SCHED_OTHER

-r

--rr

Set schedule to SCHED_RR

14.3.6. Changing the priority of services during the boot process

Using the systemd service, it is possible to set up real-time priorities for services launched during the boot process. The unit configuration directives are used to change the priority of a service during the boot process.

The boot process priority change is done by using the following directives in the service section:

CPUSchedulingPolicy=
Sets the CPU scheduling policy for executed processes. It is used to set other, fifo, and rr policies.
CPUSchedulingPriority=
Sets the CPU scheduling priority for executed processes. The available priority range depends on the selected CPU scheduling policy. For real-time scheduling policies, an integer between 1 (lowest priority) and 99 (highest priority) can be used.

The following procedure describes how to change the priority of a service, during the boot process, using the mcelog service.

Prerequisites

  1. Install the tuned package:

    # yum install tuned
  2. Enable and start the tuned service:

    # systemctl enable --now tuned

Procedure

  1. View the scheduling priorities of running threads:

    # tuna --show_threads
                          thread       ctxt_switches
        pid SCHED_ rtpri affinity voluntary nonvoluntary             cmd
      1      OTHER     0     0xff      3181          292         systemd
      2      OTHER     0     0xff       254            0        kthreadd
      3      OTHER     0     0xff         2            0          rcu_gp
      4      OTHER     0     0xff         2            0      rcu_par_gp
      6      OTHER     0        0         9            0 kworker/0:0H-kblockd
      7      OTHER     0     0xff      1301            1 kworker/u16:0-events_unbound
      8      OTHER     0     0xff         2            0    mm_percpu_wq
      9      OTHER     0        0       266            0     ksoftirqd/0
    [...]
  2. Create a supplementary mcelog service configuration directory file and insert the policy name and priority in this file:

    # cat <<-EOF > /etc/systemd/system/mcelog.system.d/priority.conf
    
    >
    [SERVICE]
    CPUSchedulingPolicy=_fifo_
    CPUSchedulingPriority=_20_
    EOF
  3. Reload the systemd scripts configuration:

    # systemctl daemon-reload
  4. Restart the mcelog service:

    # systemctl restart mcelog

Verification steps

  • Display the mcelog priority set by systemd issue:

    # tuna -t mcelog -P
    thread       ctxt_switches
      pid SCHED_ rtpri affinity voluntary nonvoluntary             cmd
    826     FIFO    20  0,1,2,3        13            0          mcelog

Additional resources

14.3.7. Priority map

Priorities are defined in groups, with some groups dedicated to certain kernel functions.

Table 14.2. Description of the priority range

PriorityThreadsDescription

1

Low priority kernel threads

This priority is usually reserved for the tasks that need to be just above SCHED_OTHER.

2 - 49

Available for use

The range used for typical application priorities.

50

Default hard-IRQ value

 

51 - 98

High priority threads

Use this range for threads that execute periodically and must have quick response times. Do not use this range for CPU-bound threads as you will starve interrupts.

99

Watchdogs and migration

System threads that must run at the highest priority.

14.3.8. cpu-partitioning profile

The cpu-partitioning profile is used to isolate CPUs from system level interruptions. Once you have isolated these CPUs, you can allocate them for specific applications. This is very useful in low-latency environments or in environments where you wish to extract the maximum performance from your hardware.

This profile also lets you designate housekeeping CPUs. A housekeeping CPU is used to run all services, daemons, shell processes, and kernel threads.

You can configure the cpu-partitioning profile in the /etc/tuned/cpu-partitioning-variables.conf file using the following configuration options:

isolated_cores=cpu-list
Lists CPUs to isolate. The list of isolated CPUs is comma-separated or you can specify a range using a dash, such as 3-5. This option is mandatory. Any CPU missing from this list is automatically considered a housekeeping CPU.
no_balance_cores=cpu-list
Lists CPUs which are not considered by the kernel during system wide process load-balancing. This option is optional. This is usually the same list as isolated_cores.

14.3.9. Additional resources

  • For more information, see the man pages of sched, sched_setaffinity, sched_getaffinity, sched_setscheduler, sched_getscheduler, cpuset, tuna, chrt, systemd, and tuned-profiles-cpu-partitioning.

14.4. Configuring kernel tick time

By default, Red Hat Enterprise Linux 8 uses a tickless kernel, which does not interrupt idle CPUs in order to reduce power usage and allow new processors to take advantage of deep sleep states.

Red Hat Enterprise Linux 8 also offers a dynamic tickless option, which is useful for latency-sensitive workloads, such as high performance computing or realtime computing. By default, the dynamic tickless option is disabled. This procedure describes how to persistently enable dynamic tickless behavior.

Procedure

  1. To enable dynamic tickless behavior in certain cores, specify those cores on the kernel command line with the nohz_full parameter. On a 16 core system, append this parameter in the /etc/default/grub file:

    nohz_full=1-15

    This enables dynamic tickless behavior on cores 1 through 15, moving all timekeeping to the only unspecified core (core 0).

  2. To persistently enable the dynamic tickless behavior, regenerate the GRUB2 configuration using the edited default file. On systems with BIOS firmware, execute the following command:

    # grub2-mkconfig -o /etc/grub2.cfg

    On systems with UEFI firmware, execute the following command:

    # grub2-mkconfig -o /etc/grub2-efi.cfg
  3. When the system boots, manually move the rcu threads to the non-latency-sensitive core, in this case core 0:

    # for i in `pgrep rcu[^c]` ; do taskset -pc 0 $i ; done
  4. Optional: Use the isolcpus parameter on the kernel command line to isolate certain cores from user-space tasks.
  5. Optional: Set the CPU affinity for the kernel’s write-back bdi-flush threads to the housekeeping core:

    echo 1 > /sys/bus/workqueue/devices/writeback/cpumask

Verification steps

  • Once the system is rebooted, verify if dynticks are enabled:

    # grep dynticks var/log/dmesg
    [    0.000000] NO_HZ: Full dynticks CPUs: 2-5,8-11
  • Verify that the dynamic tickless configuration is working correctly:

    # perf stat -C 1 -e irq_vectors:local_timer_entry taskset -c 1 stress -t 1 -c 1

    Here, stress is a program that spins on the CPU for 1 second.

  • One possible replacement for stress is a script that executes:

    while :; do d=1; done

    The default kernel timer configuration shows 1000 ticks on a busy CPU:

    # perf stat -C 1 -e irq_vectors:local_timer_entry taskset -c 1 stress -t 1 -c 1
    1000 irq_vectors:local_timer_entry
  • With the dynamic tickless kernel configured, you should see 1 tick instead:

    # perf stat -C 1 -e irq_vectors:local_timer_entry taskset -c 1 stress -t 1 -c 1
    1 irq_vectors:local_timer_entry

14.5. Setting interrupt affinity systems

An interrupt request or IRQ is a signal for immediate attention sent from a piece of hardware to a processor. Each device in a system is assigned one or more IRQ numbers which allow it to send unique interrupts. When interrupts are enabled, a processor that receives an interrupt request immediately pauses execution of the current application thread in order to address the interrupt request.

Because interrupt halts normal operation, high interrupt rates can severely degrade system performance. It is possible to reduce the amount of time taken by interrupts by configuring interrupt affinity or by sending a number of lower priority interrupts in a batch (coalescing a number of interrupts).

Interrupt requests have an associated affinity property, smp_affinity, which defines the processors that handle the interrupt request. To improve application performance, assign interrupt affinity and process affinity to the same processor, or processors on the same core. This allows the specified interrupt and application threads to share cache lines.

On systems that support interrupt steering, modifying the smp_affinity property of an interrupt request sets up the hardware so that the decision to service an interrupt with a particular processor is made at the hardware level with no intervention from the kernel.

14.5.1. Balancing interrupts manually

If your BIOS exports its NUMA topology, the irqbalance service can automatically serve interrupt requests on the node that is local to the hardware requesting service.

Procedure

  1. Check which devices correspond to the interrupt requests that you want to configure.
  2. Find the hardware specification for your platform. Check if the chipset on your system supports distributing interrupts.

    1. If it does, you can configure interrupt delivery as described in the following steps. Additionally, check which algorithm your chipset uses to balance interrupts. Some BIOSes have options to configure interrupt delivery.
    2. If it does not, your chipset always routes all interrupts to a single, static CPU. You cannot configure which CPU is used.
  3. Check which Advanced Programmable Interrupt Controller (APIC) mode is in use on your system:

    $ journalctl --dmesg | grep APIC

    Here,

    • If your system uses a mode other than flat, you can see a line similar to Setting APIC routing to physical flat.
    • If you can see no such message, your system uses flat mode.

      If your system uses x2apic mode, you can disable it by adding the nox2apic option to the kernel command line in the bootloader configuration.

      Only non-physical flat mode (flat) supports distributing interrupts to multiple CPUs. This mode is available only for systems that have up to 8 CPUs.

  4. Calculate the smp_affinity mask. For more information on how to calculate the smp_affinity mask, see Section 14.5.2, “Setting the smp_affinity mask”.

14.5.2. Setting the smp_affinity mask

The smp_affinity value is stored as a hexadecimal bit mask representing all processors in the system. Each bit configures a different CPU. The least significant bit is CPU 0. The default value of the mask is f, which means that an interrupt request can be handled on any processor in the system. Setting this value to 1 means that only processor 0 can handle the interrupt.

Procedure

  1. In binary, use the value 1 for CPUs that handle the interrupts. For example, to set CPU 0 and CPU 7 to handle interrupts, use 0000000010000001 as the binary code:

    Table 14.3. Binary Bits for CPUs

    CPU1514131211109876543210

    Binary

    0

    0

    0

    0

    0

    0

    0

    0

    1

    0

    0

    0

    0

    0

    0

    1

  2. Convert the binary code to hexadecimal:

    For example, to convert the binary code using Python:

    >>> hex(int('0000000010000001', 2))
    
    '0x81'

    On systems with more than 32 processors, you must delimit the smp_affinity values for discrete 32 bit groups. For example, if you want only the first 32 processors of a 64 processor system to service an interrupt request, use 0xffffffff,00000000.

  3. The interrupt affinity value for a particular interrupt request is stored in the associated /proc/irq/irq_number/smp_affinity file. Set the smp_affinity mask in this file:

    # echo mask > /proc/irq/irq_number/smp_affinity

14.5.3. Additional resources

  • For more information, see the man pages of irqbalance, journalctl, and taskset.

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