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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-189186 |
| Date | January 2016 |
| Creators | Hunt, Kristian |
| Publisher | KTH, Skolan för datavetenskap och kommunikation (CSC) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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