StreamAD: A cloud platform metrics-oriented benchmark for unsupervised online anomaly detection

Abstract

Cloud platforms, serving as fundamental infrastructure, play a significant role in developing modern applications. In recent years, there has been growing interest among researchers in utilizing machine learning algorithms to rapidly detect and diagnose faults within complex cloud platforms, aiming to improve the quality of service and optimize system performance. There is a need for online anomaly detection on cloud platform metrics to provide timely fault alerts. To assist Site Reliability Engineers (SREs) in selecting suitable anomaly detection algorithms based on specific use cases, we introduce a benchmark called StreamAD. This benchmark offers three-fold contributions: (1) it encompasses eleven unsupervised algorithms with open-source code; (2) it abstracts various common operators for online anomaly detection which enhances the efficiency of algorithm development; (3) it provides extensive comparisons of various algorithms using different evaluation methods; With StreamAD, researchers can efficiently conduct comprehensive evaluations for new algorithms, which can further facilitate research in this area. The code of StreamAD is published at https://github.com/Fengrui-Liu/StreamAD.