2022 NSDI NSDI 2022

How to diagnose nanosecond network latencies in rich end-host stacks

Abstract

Low-latency network stacks have brought down network latencies within end-hosts to the microsecond-regime. However, end-host profilers have such high overheads that they are useful only to confirm a hypothesis, not to diagnose a problem in the first place. Indeed, every one of twenty low-latency network projects we surveyed rolled their own analysis tools instead of using an existing profiler. This paper shows how to build a latency diagnosis tool with full-stack coverage and low overhead that can identify, not just confirm, sources of latency in end hosts. The unique measurement methodology reconstructs network-message lifetimes within end hosts with nanosecond precision, by reconciling CPU and NIC hardware profiling traces across multiple time domains (network and CPU). It uncovers unexpected latency sources in both kernel and user-space stacks. We demonstrate these capabilities by using our implementation, NSight, to systematically identify and remove performance overheads in memcached, reducing 99.9th percentile latency by a factor of 40 from 2:2 ms to 41 μs.

🧭 Keyword Pioneer — end-host profiling
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning