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.