Predicting Application Performance in the Cloud
dc.contributor.advisor | Yang, Xiaowei | |
dc.contributor.author | Zong, Xuanran | |
dc.date.accessioned | 2012-01-12T13:38:13Z | |
dc.date.available | 2012-01-12T13:38:13Z | |
dc.date.issued | 2011 | |
dc.department | Computer Science | |
dc.description.abstract | Despite the exceptional prominence of the cloud computing, the customers are lack of direct sense to select the cloud that delivers the best performance, due to the performance heterogeneity of each cloud provider. Existing solutions either migrate the application to each cloud and evaluate the performance individually, or benchmark each cloud along various dimensions and predict the overall performance of the application. However, the former incurs significant migration and configuration overhead, while the latter may suffer from coarse prediction accuracy. This thesis introduces two systems to address this issue. CloudProphet predicts the web application performance by tracing and replaying the on-premise resource demand on the cloud machines. DTRCP further predicts the performance for general applications. In particular, it addresses the execution path divergence manifested during replaying the on-premise resource demand. Our experiment results show that both systems can accurately predict the application performance. | |
dc.identifier.uri | ||
dc.subject | Computer science | |
dc.subject | Cloud computing | |
dc.subject | Deterministic replay | |
dc.subject | Performance prediction | |
dc.title | Predicting Application Performance in the Cloud | |
dc.type | Master's thesis |
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