A Design for multi-pricing high-performance computing system

Lung Pin Chen, Mike Kao, I-Chen Wu, Ting Han Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In high-performance computing (HPC) systems, resources are allocated to users according to configurable scheduling policies. Unfortunately, these policies often create resource fragments which reduce overall resource utilization. In this paper, we propose an HPC architecture which maximizes the resource utilization by allocating unused resource fragments for jobs which are malleable in the sense that these jobs can be created and terminated at any time without severe penalties. Hence, the whole HPC system can utilize almost 100% of its computing resources by filling its schedule with these malleable jobs. Malleable job computation can then be offered at a lower price for potential customers, allowing for multi-pricing schemes for the HPC system provider.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
EditorsWilliam Cheng-Chung Chu, Stephen Jenn-Hwa Yang, Han-Chieh Chao
PublisherIOS Press
Pages1733-1742
Number of pages10
ISBN (Electronic)9781614994831
DOIs
StatePublished - 1 Jan 2015
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 12 Dec 201414 Dec 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume274
ISSN (Print)0922-6389

Conference

ConferenceInternational Computer Symposium, ICS 2014
CountryTaiwan
CityTaichung
Period12/12/1414/12/14

Keywords

  • HPC
  • fragmentation
  • high-performance computing
  • multi-pricing
  • resource fragments

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  • Cite this

    Chen, L. P., Kao, M., Wu, I-C., & Wei, T. H. (2015). A Design for multi-pricing high-performance computing system. In W. C-C. Chu, S. J-H. Yang, & H-C. Chao (Eds.), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014 (pp. 1733-1742). (Frontiers in Artificial Intelligence and Applications; Vol. 274). IOS Press. https://doi.org/10.3233/978-1-61499-484-8-1733