eBeeMetrics: An eBPF-based Library Framework for Feedback-free Observability of QoS Metrics
Muntaka Ibnath, Mohammadreza Rezvani, Daniel Wong

TL;DR
eBeeMetrics is an eBPF-based framework that enables feedback-free, application-level QoS metric observation using system call events, reducing complexity and overhead in system management.
Contribution
It introduces a novel eBPF-based library that accurately derives QoS metrics from system calls, decoupling management runtimes from direct metric feedback.
Findings
Strong correlation with real throughput and latency metrics
Reduces instrumentation complexity and overhead
Open-source implementation available
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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Parallel Computing and Optimization Techniques · Cloud Computing and Resource Management
