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When a performance crisis occurs in a datacenter, rapid recovery requires quickly recognizing whether a similar incident occurred before, in which case a known remedy may apply, or whether the problem is new, in which case new troubleshooting is necessary. To address this issue this paper propose a new and efficient representation of the datacenter's state, a fingerprint, that scales linearly with the number of performance metrics considered and it is not affected by the number of machines. These fingerprints are generated online and then used as unique identifiers of the different types of performance crises so that one can effectively recognize previous occurrences and retrieve repair actions.
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