PonyTail: Profiling and Improving Service Tail Latency

2026-10-03

AI Summary

This study addresses the challenge of further reducing tail latency in data center request scheduling as it approaches optimality. It proposes a performance analysis methodology and toolkit tailored for discrete service times, bridging the gap left by conventional hotspot analysis in optimizing general service times. By leveraging control-flow and microarchitectural pattern analysis techniques, this work precisely identifies the root causes underlying service time outliers. The proposed approach is applied to five critical latency-sensitive services, achieving reductions of 10.7%–46% in 99th-percentile service time and 21%–5× in end-to-end latency.

Abstract

Datacenter services must meet tight tail latency service-level objectives. Research on improving tail latency focuses on reducing queuing delay by approximating optimal request scheduling policies. As request scheduling nears optimality, the primary way to further improve tail latency is to reduce the service time of requests. Unfortunately, datacenter services lack obvious hotspots for general service time optimization. We point out a new optimization opportunity in targeting the tail service time of services with dispersive service times, which are common in datacenters. To show the feasibility and benefit of this approach, we present PonyTail, a performance analysis methodology and toolkit for analyzing service-level outlier behaviors associated with tail service time. PonyTail helps performance engineers identify control-flow and microarchitectural patterns responsible for service time outliers, as well as their root causes. We apply PonyTail to analyze five latency-critical services, including an in-memory database system and an online search engine. Based on PonyTail's analysis, we optimize some of the services, improving their 99th percentile service time and latency by 10.7%--46% and 21%--$5\times$, respectively.

Problem

• tail latency
• service time
• datacenter services
• performance profiling
• outlier analysis

Innovation

• tail latency
• service time profiling
• microarchitectural patterns
• control-flow analysis
• performance optimization