Large-scale computing often consists of many speculative tasks to test hypotheses, search for insights, and review potentially finished products. For example, speculative tasks are issued by bioinformaticists comparing DNA sequences and computer graphics artists adjusting scene properties. This paper promotes a new computing model for shared clusters and grids in which researchers and end-users exploring search spaces disclose sets of speculative tasks, request results as needed, and cancel unfinished tasks if early results suggest no need to continue. Doing so matches natural usage patterns, making users more effective, and also enables a new class of schedulers. In simulation, we demonstrate how batchactive schedulers significantly reduce user-observed response times relative to conventional models in which tasks are requested one at a time (interactively) or requested in batches without specifying which are speculative. Over a range of simulated user behavior, for 20% of our simulations, user-observed response time is at least two times better under a batchactive scheduler, and about 50% better on average. Batchactive schedulers achieve such improvements by segregating tasks into two queues based on whether a task is speculative and scheduling these queues separately. Moreover, we show how user costs can be reduced under an incentive cost model of charging only for tasks whose results are requested.

29 pages

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