利用深度学习提高云微服务的性能可预测性

Q3 Computer Science

Operating Systems Review (ACM) Pub Date : 2019-07-25 DOI:10.1145/3352020.3352026

Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, Christina Delimitrou

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引用次数: 13

摘要

性能的不可预测性是采用云的主要障碍，并会对性能、成本和收入产生影响。随着云服务从单片设计过渡到微服务，可预测的性能变得更加重要。在使用微服务的系统中发生UOS违规行为后，检测这些违规行为会导致恢复时间过长，因为热点会在依赖服务之间传播和放大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer

Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting UOS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services.

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来源期刊

Operating Systems Review (ACM) Computer Science-Computer Networks and Communications

CiteScore

2.80

自引率

0.00%

发文量

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.