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Log Analysis for Failure Diagnosis and Workload Prediction in Cloud Computing
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Analys av loggfiler för feldiagnos och skattning av kommande belastning i system för molntjänster (Swedish)
Abstract [en]

The size and complexity of cloud computing systems makes runtime errors inevitable. These errors could be caused by the system having insufficient resources or an unexpected failure in the system. In order to be able to provide highly available cloud computing services it is necessary to auto- mate the resource provisioning and failure diagnosing processes as much as possible. Log files are often a good source of information about the current status of the system. In this thesis methods for diagnosing failures and predicting system workload using log file analysis are presented and the performance of different machine learning algorithms using our proposed methods are compared. Our experimental results show that classification tree and random forest algorithms are both suitable for diagnosing failures and that Support Vector Regression outperforms linear regression and regression trees when predicting disk availability and memory usage. However, we conclude that predicting CPU utilization requires further studies.

Place, publisher, year, edition, pages
Keyword [en]
log analysis machine learning failure diagnosis workload prediction automatic scaling cloud computing
National Category
Computer Science
URN: urn:nbn:se:kth:diva-189186OAI: diva2:944053
Subject / course
Computer Science
Educational program
Master of Science - Computer Science
Available from: 2016-06-30 Created: 2016-06-28 Last updated: 2016-06-30Bibliographically approved

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