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The online detection of anomalies is a vital element of operations in datacenters and in utility clouds like Amazon EC2. Given ever-increasing data center sizes coupled with the complexities of systems software, applications, and workload patterns, such anomaly detection must operate automatically at runtime and without the need for knowledge about normal or anomalous behaviors. Further, detection should function for different levels of abstraction like hardware and software, and for the multiple metrics used in cloud computing systems. This paper proposes EbAT - Entropybased Anomaly Testing - offering novel methods that detect anomalies by analyzing for arbitrary metrics their distributions rather than individual metric thresholds.
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