Title: Revenue maximisation for scheduling deadline-constrained mouldable jobs on high performance computing as a service platforms

Authors: Kuo-Chan Huang; Chun-Hao Hung; Wei Hsieh

Addresses: Department of Computer Science, National Taichung University of Education, No. 140, Minsheng Rd., West Dist., Taichung City 40306, Taiwan ' Department of Computer Science, National Taichung University of Education, No. 140, Minsheng Rd., West Dist., Taichung City 40306, Taiwan ' Department of Computer Science, National Taichung University of Education, No. 140, Minsheng Rd., West Dist., Taichung City 40306, Taiwan

Abstract: Traditionally, high-performance computing (HPC) systems usually deal with the so-called best-effort jobs which do not have deadlines and are scheduled in an as-quick-as-possible manner. Recently the concept of HPC as a service (HPCaaS) was proposed, aiming to transform HPC facilities and applications into a more convenient and accessible service model. To achieve that goal, there will be new issues to explore, such as scheduling jobs with deadlines and maximising the revenue of service providers. This paper presents a reservation-based dynamic scheduling approach for scheduling deadline-constrained mouldable jobs with the aim of maximising a service provider's revenue. The proposed approach has been evaluated with a series of simulation experiments. The experimental results indicate that our scheduling approach can achieve significantly higher revenue than previous methods. In the experiments, we also explored several research issues, including waiting queue sequencing, processor allocation decisions on time and space, admission control, and partial rescheduling.

Keywords: mouldable jobs; scheduling; deadline-constrained jobs; high performance computing; revenue maximisation; processor allocation.

DOI: 10.1504/IJHPCN.2018.088874

International Journal of High Performance Computing and Networking, 2018 Vol.11 No.1, pp.1 - 13

Received: 30 Jun 2015
Accepted: 22 Dec 2015
Published online: 22 Dec 2017 *

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