Characterizing In-Kernel Observability of Latency-Sensitive Request-Level Metrics with eBPF
Mohammadreza Rezvani, Ali Jahanshahi, Daniel Lin-Kit Wong · 2024
This paper explores a novel server observability approach using eBPF (extended Berkeley Packet Filter) for detailed request-level performance metrics of data center latency-sensitive applications. Utilizing eBPF system call tracing, we evaluate if syscall activity can reconstruct high-level application behaviors and bypass the need for direct userspace reporting of performance metrics. Through careful selection of eBPF events, we demonstrate that certain syscall statistics can provide robust insight into request-level metrics. In addition, we demonstrate that these metrics can also be robust to networking effects, such as packet loss. By demonstrating the ability for eBPF to provide request-level observability, we can potentially enable many non-intrusive, low-overhead use cases for feedback in system management runtime frameworks, such as resource allocation, scheduling, and power management.