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Power-aware scheduling has become a critical research thrust for deploying exascale High Performance Computing (HPC) systems with limited power budget. Time-varying pricing of electricity with respect to the market demand and dynamic HPC workloads can lead to unpredictable operational cost, which complicates the scheduling decisions further. For an oversubscribed HPC system, value based scheduling...
Task scheduling for large-scale computing systems is a challenging problem. From the users perspective, the main concern is the performance of the submitted tasks, whereas, for the cloud service providers, reducing operation cost while providing the required service is critical. Therefore, it is important for task scheduling mechanisms to balance users’ performance requirements and energy efficiency...
We design resource management heuristics that assign serial tasks to the nodes of a heterogeneous high performance computing (HPC) system. The value of completing these tasks is modeled using monotonically decreasing utility functions that represent the time-varying importance of the task. The value of completing a task is equal to its utility function at the time of its completion. The overall performance...
Task scheduling for large scale computing systems is a challenging problem. From the users’ perspective, the main concern is the performance of the submitted tasks, whereas, for the cloud service providers, reducing cost while providing the required service is critical. Therefore, there is a need for task scheduling mechanisms that balance users’ performance requirements while being energy efficient...
The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of such a system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 hours). To maximize...
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