eBeeMetrics: An eBPF-based Library Framework for Feedback-free Observability of QoS Metrics

2026-03-26

AI Summary

Existing system runtimes struggle to efficiently obtain application-level QoS metrics—such as tail latency and throughput—because these cannot be directly observed via hardware counters or kernel interfaces, leading to complex and costly feedback mechanisms. This work proposes the first non-intrusive eBPF-based framework that accurately infers application-layer QoS metrics using only kernel-observable events like system calls, combined with lightweight modeling techniques. The approach requires no application instrumentation or runtime feedback, is fully decoupled from the system runtime, and supports plug-and-play deployment. Evaluated across diverse latency-sensitive workloads, the framework achieves high correlation with ground-truth QoS metrics while significantly reducing application intrusiveness. The implementation has been open-sourced to facilitate adoption and further research.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Applications
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Many system management runtimes (SMRs), such as resource management and power management techniques, rely on quality-of-service (QoS) metrics, such as tail latency or throughput, as feedback. These QoS metrics are generally neither observable with hardware performance counters nor directly observable within the OS kernel. This introduces complexity and overhead in instrumenting the application and integrating QoS performance metric feedback with many management runtimes. To bridge this gap, we introduced eBeeMetrics, an eBPF-based library framework to accurately observe application-level metrics derived from only eBPF-observable events, such as system calls. eBeeMetrics can be used as a drop-in replacement to decouple system management runtimes from QoS metric feedback reporting, or can supplement existing QoS metrics to better identify server-side dynamics. eBeeMetrics achieves a strong correlation with real-world measured throughput and latency metrics across various latency-sensitive workloads. The eBeeMetrics tool is open-source; the source code is available at: https://github.com/Ibnathism/eBeeMetrics.

Problem

• QoS metrics
• system management runtimes
• observability
• tail latency
• throughput

Innovation

• eBPF
• QoS metrics
• feedback-free observability
• system management runtime
• tail latency