{"id":158188,"date":"2009-07-05T00:00:00","date_gmt":"2009-07-05T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/fingerprinting-the-datacenter-automated-classification-of-performance-crises\/"},"modified":"2018-10-16T19:57:41","modified_gmt":"2018-10-17T02:57:41","slug":"fingerprinting-the-datacenter-automated-classification-of-performance-crises","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/fingerprinting-the-datacenter-automated-classification-of-performance-crises\/","title":{"rendered":"Fingerprinting the datacenter: Automated classification of performance crises"},"content":{"rendered":"
\n

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 we propose a new and ef\ufb01cient representation of the datacenter\u2019s state, a \ufb01ngerprint, that scales linearly with the number of performance metrics considered and it is not affected by the number of machines. These \ufb01ngerprints are generated online and then used as unique identi\ufb01ers of the different types of performance crises so that we can effectively recognize previous occurrences and retrieve repair actions. We evaluate our approach on a production datacenter with hundreds of machines running a 24×7 enterprise – classuser – facing application, verifying each identi\ufb01cation result with the operators of the datacenter and trouble-shooting tickets. Our approach has 80% identi\ufb01cation accuracy in the operations-online setting with time to detection below 10 minutes (our operators stated that even 30 minutes into the crises is desirable), and of\ufb02ine identi\ufb01cation on the order of high 90%. To the best of our knowledge this is the \ufb01rst time such an approach has been applied to a large-scale production installation with such rigorous veri\ufb01cation. We compare our approach and show it is superior to various alternatives to the construction of a \ufb01ngerprint including an adaptation to the datacenter setting of the signatures work innatives to the construction of a fingerprint including an adaptation to the datacenter setting of the signatures work in.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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 we propose a new and ef\ufb01cient representation of the datacenter\u2019s state, 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