Online Anomaly Prediction for Robust Cluster Systems

In this paper, we present a stream-based mining algorithm for online anomaly prediction. Many real-world applications such as data stream analysis requires continuous cluster operation. Unfortunately, today's large-scale cluster systems are still vulnerable to various software and hardware problems. System administrators are often overwhelmed by the tasks of correcting various system anomalies such as processing bottlenecks (i.e., full stream buffers), resource hot spots, and service level objective (SLO) violations. Our anomaly prediction scheme raises early alerts for impending system anomalies and suggests possible anomaly causes. Specifically, we employ Bayesian classification methods to capture different anomaly symptoms and infer anomaly causes. Markov models are introduced to capture the changing patterns of different measurement metrics. More importantly, our scheme combines Markov models and Bayesian classification methods to predict when a system anomaly will appear in t...

• Type: Conference
• Year: 2009
• Where: ICDE
• Authors: Xiaohui Gu, Haixun Wang
• Keywords: Bayesian Classification Methods, Continuous Cluster Operation, Database, ICDE 2009, Online Anomaly Prediction, Stream Processing Cluster, Stream-based Mining Algorithm