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Article

Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers

1
Department of Computer Engineering, Rochester Institute of Technology, New York, NY 14623, USA
2
Department of Electronics and Computer Science (ECS), University of Southampton, Southampton SO171BJ, UK
3
School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester CO43SQ, UK
4
Department of Electronic and Software Systems, University of Southampton, Southampton SO171BJ, UK
5
School of Software Engineering, South China University of Technology, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Low Power Electron. Appl. 2020, 10(4), 32; https://doi.org/10.3390/jlpea10040032
Received: 13 August 2020 / Revised: 26 September 2020 / Accepted: 29 September 2020 / Published: 14 October 2020
Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%. View Full-Text
Keywords: high performance computing; data centers; resource allocation; profit; energy consumption; machine learning; reinforcement learning; server consolidation high performance computing; data centers; resource allocation; profit; energy consumption; machine learning; reinforcement learning; server consolidation
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MDPI and ACS Style

Mamun, S.A.; Gilday, A.; Singh, A.K.; Ganguly, A.; Merrett, G.V.; Wang, X.; Al-Hashimi, B.M. Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers. J. Low Power Electron. Appl. 2020, 10, 32. https://doi.org/10.3390/jlpea10040032

AMA Style

Mamun SA, Gilday A, Singh AK, Ganguly A, Merrett GV, Wang X, Al-Hashimi BM. Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers. Journal of Low Power Electronics and Applications. 2020; 10(4):32. https://doi.org/10.3390/jlpea10040032

Chicago/Turabian Style

Mamun, Sayed A., Alexander Gilday, Amit K. Singh, Amlan Ganguly, Geoff V. Merrett, Xiaohang Wang, and Bashir M. Al-Hashimi 2020. "Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers" Journal of Low Power Electronics and Applications 10, no. 4: 32. https://doi.org/10.3390/jlpea10040032

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