AIOps Monitoring for Kubernetes and Serverless
- Kubernetes and OpenShift containers are complicated to set up, monitor and maintain. Its components involve API server, Kube controller, kubelet and Kube scheduler. It develops faster eliminating infrastructure development. Ease of deployment and issue resolution, rapid test cycles delivers high-quality code reducing operational cost.
- Monitoring of Cluster Nodes to monitor resource utilization across the nodes that allow pod scheduling by keeping track of the pod counts, visualization of pods deployment to each node and evicted pods.
- Monitor Pod Evictions to check cluster health, manage garbage collection, review Pod issues, Scale down and Excessive load, Event Management, and Root Cause Analysis.
Challenge for Building Monitoring and Alerting Platform
- Real-Time solution to monitor the load from overview to depth on Kubernetes cluster.
- Real-Time alerting platform to generate Real-Time alerts as soon as data ingested into the platform.
- Centralized dashboard to define the rules by metrics receiving into the platform, alerting platform uses rules dynamically and integrates with Slack, Email, mobile devices, and web dashboard.
- Logs aggregation feature to view all correlated logs at particular timestamp at single place.
- Anomaly detection engine to view Real-Time fluctuations in monitoring data, detect anomalies in cluster health and performance.
- Predictive Analysis Engine, so that we could predict when our cluster usage can go high or down, and we can scale up or down before our cluster nodes crashed.