### Capturing, indexing, clustering, and retrieving system historyCapturing, indexing, clustering, and retrieving system history

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 Author Zhang, Steve ♦ Symons, Julie ♦ Goldszmidt, Moises ♦ Fox, Armando ♦ Kelly, Terence ♦ Cohen, Ira Source ACM Digital Library Content type Text Publisher Association for Computing Machinery (ACM) File Format PDF Language English
 Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science Subject Keyword Information retrieval ♦ Bayesian networks ♦ Performance objectives ♦ Clustering ♦ Signatures Abstract We present a method for automatically extracting from a running system an indexable signature that distills the essential characteristic from a system state and that can be subjected to automated clustering and similarity-based retrieval to identify when an observed system state is similar to a previously-observed state. This allows operators to identify and quantify the frequency of recurrent problems, to leverage previous diagnostic efforts, and to establish whether problems seen at different installations of the same site are similar or distinct. We show that the naive approach to constructing these signatures based on simply recording the actual raw'' values of collected measurements is ineffective, leading us to a more sophisticated approach based on statistical modeling and inference. Our method requires only that the system's metric of merit (such as average transaction response time) as well as a collection of lower-level operational metrics be collected, as is done by existing commercial monitoring tools. Even if the traces have no annotations of prior diagnoses of observed incidents (as is typical), our technique successfully clusters system states corresponding to similar problems, allowing diagnosticians to identify recurring problems and to characterize the syndrome'' of a group of problems. We validate our approach on both synthetic traces and several weeks of production traces from a customer-facing geoplexed 24 x 7 system; in the latter case, our approach identified a recurring problem that had required extensive manual diagnosis, and also aided the operators in correcting a previous misdiagnosis of a different problem. Description Affiliation: Stanford University, Palo Alto, CA (Zhang, Steve) || Hewlett-Packard Laboratories, Palo Alto, CA (Cohen, Ira; Goldszmidt, Moises; Symons, Julie; Kelly, Terence; Fox, Armando) Age Range 18 to 22 years ♦ above 22 year Educational Use Research Education Level UG and PG Learning Resource Type Article Publisher Date 1975-04-01 Publisher Place New York Journal ACM SIGOPS Operating Systems Review (OPSR) Volume Number 39 Issue Number 5 Page Count 14 Starting Page 105 Ending Page 118

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Source: ACM Digital Library