O-MAP: A per-component online anomaly predicting method for Cloud infrastructure

Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. It will be a daunting task for system administrators to manually keep track of the execution status of a large number of virtual machines all the time. Anomaly prediction is an effective approach to enhancing availability and reliability of Cloud infrastructures. In this paper, we propose O-MAP, a supervised online anomaly prediction scheme based on Hidden Markov Model (HMM). Our algorithm is basically distributed and runs locally on each computing machine on the Cloud in order to achieve high scalability. Experiments performed on real data sets validate the fact that our algorithm can achieve high prediction accuracy for a range of system anomalies with low overhead to the infrastructure in Cloud.