Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

Author:

Li Yangguang1,Jiang Zhen Ming (Jack)1,Li Heng2,Hassan Ahmed E.2,He Cheng3,Huang Ruirui3,Zeng Zhengda3,Wang Mian3,Chen Pinan3

Affiliation:

1. York University, Toronto, Ontario, Canada

2. Queen's University

3. Alibaba Group, Hangzhou, Zhejiang Province, PRC

Abstract

Many software services today are hosted on cloud computing platforms, such as Amazon EC2, due to many benefits like reduced operational costs. However, node failures in these platforms can impact the availability of their hosted services and potentially lead to large financial losses. Predicting node failures before they actually occur is crucial, as it enables DevOps engineers to minimize their impact by performing preventative actions. However, such predictions are hard due to many challenges like the enormous size of the monitoring data and the complexity of the failure symptoms. AIOps ( A rtificial I ntelligence for IT Op eration s ), a recently introduced approach in DevOps, leverages data analytics and machine learning to improve the quality of computing platforms in a cost-effective manner. However, the successful adoption of such AIOps solutions requires much more than a top-performing machine learning model. Instead, AIOps solutions must be trustable, interpretable, maintainable, scalable, and evaluated in context. To cope with these challenges, in this article we report our process of building an AIOps solution for predicting node failures for an ultra-large-scale cloud computing platform at Alibaba. We expect our experiences to be of value to researchers and practitioners, who are interested in building and maintaining AIOps solutions for large-scale cloud computing platforms.

Funder

Alibaba Innovative Research Program

Publisher

Association for Computing Machinery (ACM)

Subject

Software

Reference47 articles.

1. Practical scrubbing: Getting to the bad sector at the right time

2. Random search for hyper-parameter optimization;Bergstra James;Journal of Machine Learning Research 13,2012

Cited by 42 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Easy-to-Monitor Networks: Network Design and Measurement Path Construction;IEEE Transactions on Network Science and Engineering;2024-09

2. On the Model Update Strategies for Supervised Learning in AIOps Solutions;ACM Transactions on Software Engineering and Methodology;2024-08-26

3. Industrial adoption of machine learning techniques for early identification of invalid bug reports;Empirical Software Engineering;2024-07-31

4. A Brief Review on Prediction Methods for Cloud Resource Management;2024 9th IEEE International Conference on Smart Cloud (SmartCloud);2024-05-10

5. Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World;Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI;2024-04-14

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3