2026-02-13 –, VYAS - G - Room#VY016
Looking to monitor containers without deploying heavyweight observability stacks? This talk shows you how to build a fast, resource-efficient performance monitoring setup using Performance Co-Pilot (PCP) and Grafana - perfect for local development, testing, or lightweight production use cases.
With containers becoming the default unit of software delivery, visibility into their performance is more important than ever. Yet, most developers and sysadmins skip observability during early stages because traditional tools are too complex or resource-intensive for smaller environments.
What You’ll Learn
This talk introduces a minimal, practical observability pipeline that runs entirely on your laptop or dev machine. We’ll walk through:
- Installing and configuring PCP to collect system metrics like CPU usage, memory pressure, disk I/O, and network activity.
- Streaming and storing performance data locally for real-time and historical analysis.
- Visualizing metrics in Grafana with intuitive dashboards that help answer real questions: Why is my container slow? Is my system under load? Where’s the bottleneck?
- Observing container workloads directly from the host, without needing Kubernetes or Prometheus. We'll cover how to access per-container resource usage from a system-level view using standard Fedora/RHEL tools.
Who Should Attend
This session is ideal for:
- Developers running containers locally and looking to improve visibility into resource usage.
- System administrators exploring alternatives to heavyweight monitoring stacks.
- Anyone curious about observability fundamentals—sampling, storing, and visualizing performance metrics—with tools that are built into the Linux ecosystem.
Why It Matters
You’ll leave with a working observability stack you can take home—easy to install, easy to understand, and powerful enough to support real-world container debugging and performance monitoring. No prior experience with PCP or Grafana is required.
Whether you're building software, running test environments, or supporting edge workloads, this talk will help you see your system more clearly—without the noise.
Ayushi is an Associate Software Engineer at Red Hat, specializing in AI, ML, and open source. With over 2 years of experience, she has worked on rule writing, log analysis, and proactive issue resolution. Ayushi is also a mentor and public speaker, frequently sharing insights on open source contributions, AI advancements, and career growth in tech. Passionate about scalable automation and AI-driven support systems, she strives to bridge the gap between engineering efficiency and real-world problem-solving.