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Job scheduling on large-scale systems is an increasingly complicated affair, with numerous factors influencing scheduling policy. Addressing these concerns results in sophisticated scheduling policies that can be difficult to reason about. In this paper, we present a general utility-based scheduling framework to balance various scheduling requirements and priorities. It enables system owners to customize scheduling policies under different circumstances without changing the scheduling code. We also develop a fault-aware job allocation strategy for Blue Gene/P systems to address the increasing concern of system failures. We demonstrate the effectiveness of these facilities by means of event-driven simulations with real job traces collected from the production Blue Gene/P system at Argonne National Laboratory.

The research literature to date mainly aimed at reducing energy consumption in HPC environments. In this paper we propose a job power aware scheduling mechanism to reduce HPC's electricity bill without degrading the system utilization. The novelty of our job scheduling mechanism is its ability to take the variation of electricity price into consideration as a means to make better decisions of the timing of scheduling jobs with diverse power profiles. We verified the effectiveness of our design by conducting trace-based experiments on an IBM Blue Gene/P and a cluster system as well as a case study on Argonne's 48-rack IBM Blue Gene/Q system. Our preliminary results show that our power aware algorithm can reduce electricity bill of HPC systems as much as 23%.

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