Carrera: Enabling High-Performance eBPF-based Sketches in Network Measurement

Xiang Chen, Xin Yao, Jiayu Li, Longlong Zhu, Linying Zheng, Hongyan Liu, Jianshan Zhang, Dong Zhang, Xuan Liu, Qun Huang, Haifeng Zhou, Chunming Wu

Abstract: To achieve dynamic network measurement, trends build sketches on eBPF to avoid service interruptions. However, existing eBPF-based sketches suffer from high CPU consumption, leading to poor throughput and high latency and making them hard to measure high-speed traffic. Optimizing their performance requires users to refactor codes based on each sketch’s characteristics on eBPF, which is highly complex and time-consuming.In this paper, we argue that users should write sketches without concerning low-level eBPF performance optimizations, with the deployment automatically activating cross-sketch performance optimizations. We present Carrera, a library that offers domain-specific optimizations for eBPF-based sketches. Our contributions are (1) systematically analyzing the performance bottlenecks of eBPF-based sketches through microbenchmarks, (2) identifying practical optimizations, including hardware offloading, SIMD-accelerated hashing, traffic-aware flow index caching, prefetched randomization, and active data collection, to address the identified bottlenecks in eBPF-based sketches, (3) evaluating these optimizations with state-of-the-art sketches and demonstrating that Carrera improves throughput by up to 65% and reduces latency by up to 93% via testbed experiments.

External IDs:dblp:conf/icnp/ChenYLZZLZZLHZW25