Efficient and Accurate Anomaly Identification Using Reduced Metric Space in Utility Clouds

The online detection of anomalies is a vital element of operations in utility clouds. Detection should function for different levels of abstraction including hardware and software, and for the various metrics used in cloud computing systems. Given ever-increasing cloud sizes coupled with the complexity of system components, continuous monitoring leads to the overwhelming volume of data collected by health monitoring tools. High metric dimensionality and existence of interacting metrics compromise the detection accuracy and lead to high detection complexity. In this paper, we present a metric selection framework and propose systematic approaches to effectively identify and select the most essential metrics for online anomaly detection in utility clouds. Specifically, a mutual information based approach selects metrics with the maximized mutual relevance and the minimized redundancy. Then metric space combination and separation are explored to reduce the metric dimensionality further. Experimental results on utility cloud scenarios demonstrate the viability and efficiency of this framework. The selected metrics contribute to a high efficiency and accuracy in anomaly detection.