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Workload Prediction for Runtime Resource Management
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
2017 (English)In: 2017 IEEE NORDIC CIRCUITS AND SYSTEMS CONFERENCE (NORCAS): NORCHIP AND INTERNATIONAL SYMPOSIUM OF SYSTEM-ON-CHIP (SOC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

An intelligent resource manager is an essential part of platforms based on heterogeneous architectures. The resource manager should be able to accurately predict the future workload of the system at hand and take it into consideration for making decisions. In this paper, we study a large computer cluster and show that there exist patterns in the sequence of applications that each user runs over time, and that these patterns can be used for modeling and prediction of the applications that will be requested in the future. To this end, we develop a predictive technique based on the n-gram model. It is shown that, due to the varied nature of application sequences of different users, a universal model does not provide optimal results, and a customized model should be constructed for each user. The experimental results show that the straightforward methods have a prediction accuracy below 16% when assessed on a real-life data set. Our technique provides an accuracy improvement of more than 51% in comparison with the straightforward method.

Place, publisher, year, edition, pages
IEEE , 2017.
Keywords [en]
N-gram; prediction; resource manager; statistical information; workload
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-145842DOI: 10.1109/NORCHIP.2017.8124965ISI: 000425049100024ISBN: 978-1-5386-2844-7 OAI: oai:DiVA.org:liu-145842DiVA, id: diva2:1192104
Conference
IEEE Nordic Circuits and Systems Conference (NORCAS) / NORCHIP and International Symposium of System-on-Chip (SoC)
Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-21

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf