Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems
Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems
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发表时间：
2021
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通讯作者：
Minghua Ma;Shenglin Zhang;Junjie Chen;Jim Xu;Haozhe Li;Yongliang Lin;Xiaohui Nie;Bo Zhou;Yong Wang;Dan Pei
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作者：
Minghua Ma;Shenglin Zhang;Junjie Chen;Jim Xu;Haozhe Li;Yongliang Lin;Xiaohui Nie;Bo Zhou;Yong Wang;Dan Pei
随着在线服务系统的蓬勃发展，对CPU利用率、平均响应时间和每秒请求数等多变量时间序列的异常检测对系统的可靠性至关重要。虽然已经为此目的设计了一系列基于学习的方法，但我们的实证研究表明，这些方法在足够的训练数据下初始化时间较长。本文将压缩感知技术引入到多元时间序列异常检测中，以实现快速初始化。为了构建一个跳跃启动异常检测器，我们提出了一种名为JumpStarter的方法。基于特定领域的见解，我们设计了一个基于形状的聚类算法，以及一个离群抗抽样算法的JumpStarter。使用从两家互联网公司收集的真实世界多变量时间序列数据集，我们的结果显示，JumpStarter的平均F1得分为94.12%，显著优于最先进的异常检测算法，初始化时间更短，仅为20分钟。我们已经在在线服务系统中应用了JumpStarter，并在实际场景中获得了有用的经验教训。
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 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 domainspecific 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.