使用无监督学习实现实用的云性能调试

Q3 Computer Science
Yu Gan, Mingyu Liang, Sundar Dev, David Lo, Christina Delimitrou
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引用次数: 1

摘要

摘要云应用程序正越来越多地从大型单片服务转向松散耦合微服务的复杂图。尽管微服务有好处,但它们容易出现级联性能问题,并可能导致长时间的性能下降。我们介绍了Sage,一个用于交互式云微服务的机器学习驱动的根本原因分析系统,它既准确又实用。我们表明,Sage能够正确识别各种微服务性能问题的根本原因,并采取行动解决这些问题,从而实现更可预测、更高性能、更高效的云系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling Practical Cloud Performance Debugging with Unsupervised Learning
Abstract-Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite their benefits, microservices are prone to cascading performance issues, and can lead to prolonged periods of degraded performance. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices that is both accurate and practical. We show that Sage correctly identifies the root causes of performance issues across a diverse set of microservices and takes action to address them, leading to more predictable, performant, and efficient cloud systems.
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来源期刊
Operating Systems Review (ACM)
Operating Systems Review (ACM) Computer Science-Computer Networks and Communications
CiteScore
2.80
自引率
0.00%
发文量
10
期刊介绍: Operating Systems Review (OSR) is a publication of the ACM Special Interest Group on Operating Systems (SIGOPS), whose scope of interest includes: computer operating systems and architecture for multiprogramming, multiprocessing, and time sharing; resource management; evaluation and simulation; reliability, integrity, and security of data; communications among computing processors; and computer system modeling and analysis.
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