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In modern cloud computing systems, hundreds and even thousands of cloud servers are interconnected by multi-layer networks. In such large-scale and complex systems, failures are common. Proactive failure management is a crucial technology to characterize system behaviors and forecast failure dynamics in the cloud. To make failure predictions, the authors need to monitor the system execution and collect health-related runtime performance data. However, in newly deployed or managed cloud systems, these data are usually unlabeled. Supervised learning based approaches are not suitable in this case.
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