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Network device syslogs are ubiquitous and abundant in modern data centerswith most large data centers producing millions of messages per day. Yet, theoperational information reflected in syslogs and their implications ondiagnosis or management tasks are poorly understood. Prevalent approaches tounderstanding syslogs focus on simple correlation and abnormality detection andare often limited to detection providing little insight towards diagnosis andresolution.  Towards improving data center operations, we propose and implementLog-Prophet, a system that applies a toolbox of statistical techniques anddomain-specific models to mine detailed diagnoses. Log-Prophet infers causalrelationships between syslog lines and constructs succinct but valuable problemgraphs, summarizing root causes and their locality, including cascadingproblems. We validate Log-Prophet using problem tickets and through operatorinterviews. To demonstrate the strength of Log-Prophet, we perform an initiallongitudinal study of a large online service provider's data center. Our studydemonstrates that Log-Prophet significantly reduces the number of alerts whilehighlighting interesting operational issues.

Finding Needles in the Haystack: Harnessing Syslogs for Data Center Management