一句话：提出 Sage：以机器学习驱动的微服务性能调试，避免大量插桩，兼顾实用性与可扩展性。
云应用正从单体服务转向松耦合的微服务图。模块化与弹性带来收益的同时，层间依赖会引入背压与级联 QoS 违规，令集群管理和性能调试变得复杂。已有性能调试工作或依赖经验性技术，或采用监督学习诊断性能问题根因，后者需要大量应用插桩，在实践中难以部署。本文提出 Sage，一种由机器学习驱动的微服务性能调试系统，目标是实用且可扩展，减少对应用插桩的依赖，从而更易在实际环境中落地。摘要未给出具体方法细节与实验评估结果。
Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. Prior work on performance debugging for cloud services either relies on empirical techniques, or uses supervised learning to diagnose the root causes of performance issues, which requires significant application instrumentation, and is difficult to deploy in practice.
DOI 原文 ·
@inproceedings{paperbot6142,
title = {Sage: practical and scalable ML-driven performance debugging in microservices},
author = {Yu Gan and Mingyu Liang and Sundar Dev and David Lo and Christina Delimitrou},
booktitle = {ASPLOS},
year = {2021},
doi = {10.1145/3445814.3446700}
}