research-article

Robust System Instance Clustering for Large-Scale Web Services

Authors:

Shenglin Zhang,

Yinlong LiuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1785 - 1796

https://doi.org/10.1145/3485447.3511983

Published: 25 April 2022 Publication History

Abstract

System instance clustering is crucial for large-scale Web services because it can significantly reduce the training overhead of anomaly detection methods. However, the vast number of system instances with massive time points, redundant metrics, and noise bring significant challenges. We propose OmniCluster to accurately and efficiently cluster system instances for large-scale Web services. It combines a one-dimensional convolutional autoencoder (1D-CAE), which extracts the main features of system instances, with a simple, novel, yet effective three-step feature selection strategy. We evaluated OmniCluster using real-world data collected from a top-tier content service provider providing services for one billion+ monthly active users (MAU), proving that OmniCluster achieves high accuracy (NMI=0.9160) and reduces the training overhead of five anomaly detection models by 95.01% on average.

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Cited By

Xie SWang JLi BZhang ZLi DHung P(2024)PBScaler: A Bottleneck-Aware Autoscaling Framework for Microservice-Based ApplicationsIEEE Transactions on Services Computing10.1109/TSC.2024.337620217:2(604-616)Online publication date: Mar-2024
https://doi.org/10.1109/TSC.2024.3376202
Sun YLiang MChe ZLi DZheng TZhang SZhu PZhang YPei D(2023)Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Web Services2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00033(145-151)Online publication date: Jul-2023
https://doi.org/10.1109/ICWS60048.2023.00033
Liu JJiang ZGu JHuang JChen ZFeng CYang ZYang YLyu M(2023)Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00077(268-280)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00077
Show More Cited By

Index Terms

Robust System Instance Clustering for Large-Scale Web Services
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Xie SWang JLi BZhang ZLi DHung P(2024)PBScaler: A Bottleneck-Aware Autoscaling Framework for Microservice-Based ApplicationsIEEE Transactions on Services Computing10.1109/TSC.2024.337620217:2(604-616)Online publication date: Mar-2024
https://doi.org/10.1109/TSC.2024.3376202
Sun YLiang MChe ZLi DZheng TZhang SZhu PZhang YPei D(2023)Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Web Services2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00033(145-151)Online publication date: Jul-2023
https://doi.org/10.1109/ICWS60048.2023.00033
Liu JJiang ZGu JHuang JChen ZFeng CYang ZYang YLyu M(2023)Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00077(268-280)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00077
He ZChen PHuang T(2022)Share or Not Share? Towards the Practicability of Deep Models for Unsupervised Anomaly Detection in Modern Online Systems2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00014(25-35)Online publication date: Oct-2022
https://doi.org/10.1109/ISSRE55969.2022.00014

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