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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