Predicting Application Performance in the Cloud
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.

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