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Anomaly detection is an effective approach to enhancing availability and reliability of Cloud infrastructures. Anomaly detection solution for Cloud computing systems must operate at runtime and without the need for prior knowledge about normal or anomalous behaviors. In this paper, we propose an unsupervised online anomaly detection scheme based on hidden Markov Model. 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 detection accuracy for a range of system anomalies with low overhead to the infrastructure in Cloud. 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