We describe and evaluate the Running Time Advisor (RTA), a system that can predict the running time of a compute-bound task on a typical shared, unreserved commodity host. The prediction is computed from linear time series predictions of host load and takes the form of a confidence interval that neatly expresses the error associated with the measurement and prediction processes, error that must be captured to make statistically valid decisions based on the predictions. Adaptive applications make such decisions in pursuit of consistent high performance, choosing, for example, the host where a task is most likely to meet its deadline. We begin by describing the system and summarizing the results of our previously published work on host load prediction (P.A. Dinda, 1999; 2000)We then describe our algorithm for computing predictions of running time from host load predictions. Finally, we evaluate the system using over 100000 randomized testcases run on 39 different hosts.
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