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Mapping Moving Landscapes by Mining Mountains of Logs: Novel Techniques for Dependency Model Generation
Ecole Polytechnique Federale de Lausanne.
Ecole Polytechnique Federale de Lausanne.
Ecole Polytechnique Federale de Lausanne.ORCID-id: 0000-0003-4516-7317
Geneva University Hospitals (HUG).
2006 (Engelska)Ingår i: The 32nd International Conference on Very Large Data Bases, September 12-15, 2006, Seoul, Korea., 2006, s. 1093-1102Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Problem diagnosis for distributed systems is usually difficult. Thus, an automated support is needed to identify root causes of encountered problems such as performance lags or inadequate functioning quickly. The many tools and techniques existing today that perform this task rely usually on some dependency model of the system. However, in complex and fast evolving environments it is practically unfeasible to keep such a model up-to-date manually and it has to be created in an automatic manner. For high level objects this is in itself a challenging and less studied task. In this paper, we propose three different approaches to discover dependencies by mining system logs. Our work is inspired by a recently developed data mining algorithm and techniques for collocation extraction from the natural language processing field. We evaluate the techniques in a case study for Geneva University Hospitals (HUG) and perform large-scale experiments on production data. Results show that all techniques are capable of finding useful dependency information with reasonable precision in a real-world environment.

Ort, förlag, år, upplaga, sidor
2006. s. 1093-1102
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:kth:diva-109817OAI: oai:DiVA.org:kth-109817DiVA, id: diva2:584595
Konferens
32nd international conference on Very large data bases 2006
Anmärkning

QC 20130610

Tillgänglig från: 2013-01-09 Skapad: 2013-01-09 Senast uppdaterad: 2013-06-10Bibliografiskt granskad

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http://dl.acm.org/citation.cfm?id=1164221

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