Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes efficiency metrics now work with infrastructure filters.

There is a particular kind of incident every SRE team is familiar with. A common component of your stack, say your Redis database, starts misbehaving. Someone spends two hours tracing it back to a connection pool exhausted by a misconfigured client, the fix goes in, and everyone moves on, for today. The following Tuesday it happens again, and whoever is on call investigates it from scratch, because the person who solved it last week is asleep, on vacation, or working somewhere else now.

The Kubex UI is a model of analytic depth and flexibility. You can break down your environment’s utilization, risks, and optimization potential in many ways. You can view more than 100 different properties for each container and many different graphs showing historical performance and identified trends.

It's 2:14 AM. CPU usage is normal. Memory looks stable. No pods are in CrashLoopBackOff. Every dashboard is green. And yet API latency has doubled, checkout requests are timing out, and your on-call phone won't stop buzzing. This is the defining trait of abnormal Kubernetes workload behavior: it rarely announces itself through the metrics you already watch. Kubernetes is exceptionally good at reporting whether a pod is running. It is far less good at telling you whether a pod is doing its job correctly.

In the previous post in this series, we covered why Virtual Machine (VM) Live Migration in Kubernetes is difficult: a VM’s IP is its identity, and the “new” VM on the destination node has to come up with the same IP, this something that Kubernetes is not known for, and on top of that, traffic has to switch over only after network security policies are in place.

The first stage of AI SRE maturity is a laptop, a throwaway cluster, and zero production access. Here’s how to set it up, and what to watch for. AI SRE (Site Reliability Engineering) agents are AI-powered systems that automate the most time-consuming parts of incident response: triaging alerts, correlating logs and metrics, generating root-cause hypotheses, and proposing remediation steps.