Using queue time predictions for processor allocation

When a moldable job is submitted to a space-sharing parallel computer, it must choose whether to begin execution on a small, available cluster or wait in queue for more processors to become available. To make this decision, it must predict how long it will have to wait for the larger cluster. We propose statistical techniques for predicting these queue times, and develop an allocation strategy that uses these predictions. We present a workload model based on observed workloads at the San Diego Supercomputer Center and the Cornell Theory Center, and use this model to drive simulations of various allocation strategies. We find that prediction-based allocation not only improves the turnaround time of individual jobs; it also improves the utilization of the system as a whole.