Identifying Quick Starters: Towards an Integrated Framework for Efficient Predictions of Queue Waiting Times of Batch Parallel Jobs

Abstract

Production parallel systems are space-shared and hence employ batch queues in which the jobs submitted to the systems are made to wait before execution. Thus, jobs submitted to parallel batch systems incur queue waiting times in addition to the execution times. Prediction of these queue waiting times is important to provide overall estimates to the users and can also help metaschedulers make scheduling decisions. Analyses of the job traces of supercomputers reveal that about 56 to 99% of the jobs incur queue waiting times of less than an hour. Hence, identifying these quick starters or jobs with short queue waiting times is essential for overall improvement on queue waiting time predictions. Existing strategies provide high overestimates of upper bounds of queue waiting times rendering the bounds less useful for jobs with short queue waiting times. In this work, we have developed an integrated framework that uses the job characteristics, and states of the queue and processor occupancy to identify and predict quick starters, and use the existing strategies to predict jobs with long queue waiting times. Our experiments with different production supercomputer job traces show that our prediction strategies can lead to correct identification of up to 20 times more quick starters and provide tighter bounds for these jobs, and thus result in up to 64% higher overall prediction accuracy than existing methods.