Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems


Minghua Ma, Tsinghua University, BNRist; Shenglin Zhang, Nankai University; Junjie Chen, Tianjin University; Jim Xu, Georgia Tech; Haozhe Li and Yongliang Lin, Nankai University; Xiaohui Nie, Tsinghua University, BNRist; Bo Zhou and Yong Wang, CNCERT/CC; Dan Pei, Tsinghua University, BNRist


With the booming of online service systems, anomaly detection on multivariate time series, such as a combination of CPU utilization, average response time, and requests per second, is increasingly important for system reliability. Although a collection of learning-based approaches have been designed for this purpose, our empirical study shows that these approaches suffer from long initialization time for sufficient training data. In this paper, we introduce the Compressed Sensing technique to multivariate time series anomaly detection for rapid initialization. To build a jump-starting anomaly detector, we propose an approach named JumpStarter. Based on domain-specific insights, we design a shape-based clustering algorithm as well as an outlier-resistant sampling algorithm for JumpStarter. With real-world multivariate time series datasets collected from two Internet companies, our results show that JumpStarter achieves an average F1 score of 94.12%, significantly outperforming the state-of-the-art anomaly detection algorithms, with a much shorter initialization time of twenty minutes. We have applied JumpStarter in online service systems and gained useful lessons in real-world scenarios.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {273935,
author = {Minghua Ma and Shenglin Zhang and Junjie Chen and Jim Xu and Haozhe Li and Yongliang Lin and Xiaohui Nie and Bo Zhou and Yong Wang and Dan Pei},
title = {{Jump-Starting} Multivariate Time Series Anomaly Detection for Online Service Systems},
booktitle = {2021 USENIX Annual Technical Conference (USENIX ATC 21)},
year = {2021},
isbn = {978-1-939133-23-6},
pages = {413--426},
url = {https://www.usenix.org/conference/atc21/presentation/ma},
publisher = {USENIX Association},
month = jul,

Presentation Video