Detecting Noisy Neighbors in CPU-Isolated Cgroups Environments

Control groups (cgroups) are a crucial isolation mechanism in containerized environments, but they don't fully prevent performance interference (noisy neighbors). This paper presents a novel, workload-agnostic approach for detecting noisy neighbors within CPU-isolated cgroups. Using in-kernel profiling with Extended Berkeley Packet Filter (eBPF), we instrument the Linux process scheduler to capture scheduling latencies and preemption frequencies. We introduce a detection method based on these metrics to identify noisy neighbors online without requiring workload profiles or offline analysis. Evaluations across various workload scenarios demonstrate the effectiveness of our approach in accurately identifying performance degradation caused by noisy neighbors.