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A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems

Authors :
He, Zilong
Chen, Pengfei
Li, Xiaoyun
Wang, Yongfeng
Yu, Guangba
Chen, Cailin
Li, Xinrui
Zheng, Zibin
Source :
IEEE Transactions on Neural Networks and Learning Systems; 2023, Vol. 34 Issue: 4 p1705-1719, 15p
Publication Year :
2023

Abstract

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way. Besides, with the increasing complexity of cloud systems, effectively organizing data collected from a wide range of components of a system and modeling spatiotemporal dependence among them become a challenge. In this article, we propose TopoMAD, a stochastic seq2seq model which can robustly model spatial and temporal dependence among contaminated data. We include system topological information to organize metrics from different components and apply sliding windows over metrics collected continuously to capture the temporal dependence. We extract spatial features with the help of graph neural networks and temporal features with long short-term memory networks. Moreover, we develop our model based on variational auto-encoder, enabling it to work well robustly even when trained on contaminated data. Our approach is validated on the run-time performance data collected from two representative cloud systems, namely, a big data batch processing system and a microservice-based transaction processing system. The experimental results show that TopoMAD outperforms some state-of-the-art methods on these two data sets.

Details

Language :
English
ISSN :
2162237x and 21622388
Volume :
34
Issue :
4
Database :
Supplemental Index
Journal :
IEEE Transactions on Neural Networks and Learning Systems
Publication Type :
Periodical
Accession number :
ejs62728718
Full Text :
https://doi.org/10.1109/TNNLS.2020.3027736