Red Hat’s OpenShift is a Platform as a Service (PaaS) for developing applications rapidly and deploying them at scale – with the least amount of friction and the greatest amount of automation. Because OpenShift’s underlying platform makes use of Docker and Kubernetes to run your applications and services, this can pose unique monitoring challenges if you’ve primarily dealt with monolithic applications in the past. At SignifAI, we believe there’s a better way to monitor OpenShift, with AI and machine learning powered correlations.

While OpenShift’s tooling does provide some basic metrics like container health and resource usage, many administrators quickly realize that OpenShift creates a new set of monitoring problems they need to contend with.

Detailed Metrics: How does one go about getting detailed metrics without overloading the containers with instrumentation?

Correlating OpenShift and Non-OpenShift Data: How does one go about correlating OpenShift metrics with the other operational data being generated by dependent applications and services, perhaps not even running on OpenShift? Data from tools like Prometheus, New Relic, Datadog, Dynatrace, and ElasticSearch?

More Alerts, More Problems: Finally, how can one achieve “full visibility” and ensure you are not missing anything important without drowning yourself in alerts? No one wants to make the maintenance of alert grouping and notification rules a full-time job!

SignifAI is a SaaS-based correlation engine that leverages AI and machine learning to help administrators identify root causes and solutions quickly in complex OpenShift environments. SignifAI’s automated platform requires no previous AI or machine learning expertise to use. Connecting your OpenShift monitoring tools like Prometheus and Operator Framework to the SignifAI platform is accomplished via APIs or webhooks. There are no agents to install and no data tagging is required. SignifAI automatically transforms the data to find relationships, correlations, anomalies, and trends. These results are then presented in a UI in the form of issue cards. Users also have the ability to input their own logic (“train the model”) to increase the accuracy of the correlations.

Reduced Alert Noise: SignifAI takes the complexity out of grouping Prometheus and Hawkular alerts by using AI and machine learning to automatically group related alerts and reduce Prometheus or Hawkular-specific alert noise. On top of that, SignifAI also automatically groups related alerts that might be coming from other monitoring systems. This ends up delivering real “alert noise” reduction because it takes into account all the monitoring systems in a complex OpenShift environment, not just Prometheus or Hawkular.

Efficient Root Cause Analysis: SignifAI uses AI and machine learning to identify powerful correlations across all the operational data found in a complex OpenShift environment. This gives administrators the full context surrounding an issue that they will need to conduct efficient root cause analysis. SignifAI then enriches each issue with relevant data, regardless of the timeframe, data source or data type, including Operator Framework data plus logs, events or metrics from other systems. Contrast this with Prometheus’ and Hawkular’s limited view of metrics (or Elastic logs.)

Enriched and Actionable Alerts: SignifAI enriches each issue with suggested solutions, KB articles, and links to similar issues that have been successfully resolved in the past. These enrichments enable engineers to get to an accurate solution much more quickly than by manually search for or formulating solutions from scratch. Intelligent and actionable alerts translate into faster MTTR whether you are using Prometheus, Hawkular or either of these tools in conjunction with other monitoring tools.