Open Source Summit Korea 2026

Catching transient performance drops is notoriously hard; by the time you attach a profiler, the bottleneck is often gone. Continuous tracing is the solution, but manually analyzing the resulting massive data is a huge hurdle.

This session introduces a practical methodology for always-on performance monitoring and automated analysis using Guider. First, we demonstrate how Guider efficiently profiles system performance in the background with minimal overhead. Upon detecting an anomaly, it automatically generates a retroactive report using trace data captured just before the event.

We will then show how to feed these structured reports into an AI pipeline. Instead of manually deciphering complex kernel metrics, you will see how AI instantly pinpoints the root cause of performance regressions.

Attendees will learn to build a continuous monitoring pipeline that automatically turns raw trace data into real-time, actionable insights.

There is this moment every platform team hits where an alert fires at 1am, everyone stares at it, and nobody is quite sure what it means or whose job it is to fix it.

That was us. 11 EKS clusters. 6 AWS accounts. Alerts routing to a channel. No runbooks. No context. Just noise.

Here is what made it worse --- we were the team responsible for the observability stack itself. VictoriaMetrics, vmagent, vmselect, Grafana, CloudWatch --- we ran all of it. And most of it was set up just well enough to fire alerts, but not well enough to actually help anyone during an incident.

Most observability talks are about how to instrument your applications. This one is about what happens when the platform itself becomes the thing you need to observe! and you are the one responsible for both the problem and the solution.

We will talk about what we got wrong first, what a P1 at 1am actually teaches you about your own stack, and what we built to make sure the next time something breaks, we know exactly where to look within the first five minutes.