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Utility clouds continue to grow in scale and in the complexity of their components and interactions, which introduces a key challenge to failure and resource management for highly dependable cloud computing. Autonomic anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To identify anomalies, the authors need to monitor the system execution and collect health-related runtime performance data. These data are usually unlabeled and a prior failure history is not always available in production systems, especially for newly deployed or managed utility clouds.
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