Performance Metric Selection for Autonomic Anomaly Detection on Cloud Computing Systems

With ever-growing complexity and dynamicity of cloud computing systems, dependability assurance has become a major concern in system design and management. In this paper, we propose a framework for autonomic anomaly detection in the cloud. Mutual information is exploited to quantify the relevance and redundancy among the large number of performance metrics. An incremental search algorithm is presented for metric selection. We apply principal component analysis to further reduce the metric dimension, while keeping the variance in the health- related data as much as possible. A detection mechanism with semi-supervised decision tree classifiers works on the reduce metric dimensionality and identifies anomalies. We have implemented a prototype of our autonomic anomaly detection framework and evaluated its performance on an institute-wide cloud computing system.