https://sre-ai-blog.pages.dev/en/tags/ebpf/Recent content in EBPF on SRE & AI Field NotesHugoenSun, 26 Jul 2026 01:29:48 +0800https://sre-ai-blog.pages.dev/en/posts/ebpf-observability/Fri, 10 Jul 2026 00:00:00 +0000https://sre-ai-blog.pages.dev/en/posts/ebpf-observability/<h2 id="1-why-ebpf-observability">1. Why eBPF Observability</h2> <p>Traditional observability solutions rely on application instrumentation and sidecar proxies, introducing significant performance overhead and blind spots. eBPF allows safe execution of sandboxed programs within the kernel, capturing system calls, network packets, and file operations without modifying application code or introducing additional latency.</p> <h2 id="2-three-layer-monitoring-with-ebpf">2. Three-Layer Monitoring with eBPF</h2> <p>We split observability into network, storage, and application layers. The network layer tracks TCP connection latency and retransmission rates via eBPF; the storage layer monitors file I/O and page cache hit rates; the application layer uses eBPF uprobes to capture key function call chains.</p> <h2 id="3-prometheus-federation-architecture">3. Prometheus Federation Architecture</h2> <p>For multi-cluster scenarios, we adopted the Prometheus federation model: each cluster runs an independent Prometheus Server collecting eBPF-exported metrics, with a central Prometheus aggregating them for a global cross-cluster view.</p>