PoS - Proceedings of Science
Volume 378 - International Symposium on Grids & Clouds 2021 (ISGC2021) - Converging High Performance infrastructures: Supercomputers, clouds, accelerators
Architecture of Job Scheduling Simulator for Demand Response Based Resource Provisioning
S. Matsui*, Y. Watashiba, S. Date, J. Liu, S. Shimojo and K. Harumoto
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Published on: October 22, 2021
We study a new service model based on the Demand Response (DR) resource provisioning at High Performance Computing (HPC) centers. This DR-based resource provisioning model allows administrators of HPC centers to provide computing services with incentives to users to compensate for the performance loss due to power saving operations. In a power conservation mode, a job’s performance may decrease, both in terms of a job waiting time and a job execution time. With DR-based resource provisioning, the submitted jobs are divided into two categories, allowed jobs and disallowed jobs, depending on the user’s tolerance in the performance degradation. The allowed jobs, if indeed affected by the power saving operations, will receive compensation in accordance with an incentive system which determines the reward to the user. For designing an appropriate demand response model, we need to focus on the increase in the job’s execution time and the job’s waiting time, and the corresponding decrease in the power consumption. These are important factors in deriving an incentive system. Currently, no existing approaches can reliably quantify the effectiveness and the contribution of these factors in HPC job scheduling and resource provisioning. In this paper, we propose a newly developed job scheduling simulator that can evaluate DR-based resource provisioning approach under various operating conditions. We designed and implemented the job scheduling simulator for HPC demand-response resource provisioning using a general-purpose discrete-event simulator. Our experiments show that the job scheduling simulator can properly represent the demand response resource provisioning using different job scheduling scenarios.
DOI: https://doi.org/10.22323/1.378.0006
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