Chapter 4. Configuring Resource Isolation on Hyper-Converged Nodes
With the Red Hat OpenStack Platform implementation of HCI, the director creates hyper-converged nodes by colocating Ceph OSD and Compute services. However, without any further tuning this colocation also risks resource contention between Ceph and Compute services, as neither are aware of each other’s presence on the same host. Resource contention can result in degradation of service. This, in turn, offsets any benefits provided by hyper-convergence.
To prevent contention, you need to configure resource isolation for both Ceph and Compute services. The following subsections describe how to do so.
4.1. Reserve CPU and Memory Resources for Compute
By default, the Compute service parameters do not take into account the colocation of Ceph OSD services on the same node. Hyper-converged nodes need to be tuned in order to address this to maintain stability and maximize the number of possible instances. To do this, you need to set resource constraints for the Compute service on hyper-converged nodes. You can configure this through a plan environment file.
Plan environment files define workflows, which the director can execute through the OpenStack Workflow (Mistral) service. The director also provides a default plan environment file specifically for configuring resource constraints on hyper-converged nodes, namely:
-p parameter to invoke this plan environment file during deployment (as in, to your
openstack overcloud deploy command). This plan environment file will direct OpenStack Workflow to:
- Retrieve hardware introspection data (collected during Inspecting the Hardware of Nodes),
- Calculate optimal CPU and memory constraints for Compute on hyper-converged nodes based on that data, and
- Autogenerate the necessary parameters to configure those constraints.
~/plan-samples/plan-environment-derived-params.yaml plan environment file defines several CPU and memory allocation workload profile defined under
hci_profile parameter sets which workload profile is enabled; for example, if you are using NFV, set
You can also define a custom profile in your own plan environment file using the same syntax. For example, to define a new profile named
average_guest_cpu_utilization_percentage parameters in each workload profile will calculate values for the
cpu_allocation_ratio settings of Compute. These values are calculated based on Red Hat recommendations, and are similar to calculations made manually in previous releases (in particular, Reserve CPU and Memory Resources for Compute).
4.1.1. Override Calculated Settings for Memory or CPU Allocation
You can override the Compute settings automatically defined by OpenStack Workflow through another environment file. This is useful if you want to only override either
cpu_allocation_ratio and let OpenStack Workflow define the other. Consider the following snippet:
parameter_defaults: ComputeHCIParameters: NovaReservedHostMemory: 181000 1 ComputeHCIExtraConfig: nova::cpu_allocation_ratio: 8.2 2
NovaReservedHostMemoryparameter sets how much RAM should be reserved for the Ceph OSD services and per-guest instance overhead on hyper-converged nodes.
nova::cpu_allocation_ratio:parameter sets the ratio that the Compute scheduler should use when choosing which Compute node to deploy an instance.
ComputeHCIExtraConfig hooks apply their nested parameters to all nodes that use the
ComputeHCI role (namely, all hyper-converged nodes). For more information about manually determining optimal values for
nova::cpu_allocation_ratio:, see Section A.2, “Compute CPU and Memory Calculator”.
4.2. Reduce Ceph Backfill and Recovery Operations
When a Ceph OSD is removed, Ceph uses backfill and recovery operations to rebalance the cluster. Ceph does this to keep multiple copies of data according to the placement group policy. These operations use system resources. If a Ceph cluster is under load its performance will drop as it diverts resources to backfill and recovery.
To mitigate this performance effect during OSD removal, you can reduce the priority of backfill and recovery operations. Keep in mind that the trade off for this is that there are less data replicas for a longer time, which puts the data at a slightly greater risk.
To configure the priority of backfill and recovery operations, add an environment file named
~/templates containing the following:
parameter_defaults: CephConfigOverrides: osd_recovery_op_priority: 3 1 osd_recovery_max_active: 3 2 osd_max_backfills: 1 3
osd_recovery_op_prioritysets the priority for recovery operations, relative to the OSD client OP priority.
osd_recovery_max_activesets the number of active recovery requests per OSD, at one time. More requests will accelerate recovery, but the requests place an increased load on the cluster. Set this to
1if you want to reduce latency.
osd_max_backfillssets the maximum number of backfills allowed to or from a single OSD.
The values used in this sample are the current defaults. You do not need to add
ceph-backfill-recovery.yaml to your deployment unless you plan to use different values.
4.3. Reserving Memory Resources for Ceph
In hyper-converged deployments, there is contention for memory resources between Compute (nova) and Ceph processes. Hyper-converged deployments of Red Hat OpenStack Platform Red Hat Ceph Storage (RHCS) should use ceph-ansible 3.2 and newer, because it automatically tunes Ceph memory settings. BlueStore is the recommended back end for hyper-converged deployments because of its memory handling features.
Red Hat does not recommend directly overriding the
As of ceph-ansible 3.2, the
ceph_osd_docker_memory_limit is set automatically to the maximum memory of the host, as discovered by Ansible, regardless of whether the FileStore or BlueStore back end is used.
osd_memory_target parameter is the preferred way to reduce memory growth by Ceph OSDs, and it was introduced for BlueStore in RHCS 3.2. Ceph-ansible automatically sets this parameter, and ceph-ansible adjusts the setting for hyper-converged infrastructures (HCI) deployments if the new
is_hci parameter is set to
true as shown in the example:
parameter_defaults: CephAnsibleExtraConfig: is_hci: true
Save this setting in
4.4. Reserving CPU Resources for Ceph
In hyper-converged deployments there is contention for CPU resources between Compute (nova) and Ceph processes. By default,
ceph-ansible limits each OSD to one vCPU by using the
--cpu-quota option of the
docker run command. The following example overrides the default so that two vCPUs are available for each OSD:
parameter_defaults: CephAnsibleExtraConfig: ceph_osd_docker_cpu_limit: 2
If more than one CPU per OSD is required, set the
ceph_osd_docker_cpu_limit to the desired limit and save it in
The values used in this example show how to tune CPU resources per OSD. What the tuned value should be varies based on hardware and workload. See the Red Hat Ceph Storage Hardware Guide for guidelines and always test workloads before sending them into production.