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Two-Phase Interarrival Time 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 EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD), IEEE , 2017, p. 524-528Conference paper, Published paper (Refereed)
Abstract [en]

Platforms that are based on heterogeneous architectures require an intelligent resource manager. An intelligent resource manager should be able to accurately predict the future workload of the system at hand and take it into consideration. In this paper, we show that there exist patterns in the interarrival times of resource requests, and that these patterns can be used for modeling and prediction of the future arrivals. To this end, we develop a two-phase machine-learning-based framework and apply it to real data. First, in the offline phase of our framework, the interarrival times are clustered based on a number of extracted features, and then an adequate modeling and prediction method is selected for each detected cluster. It is shown that, due to the intricate and varied nature of interarrival times, a universal modeling and prediction method does not provide optimal results, and a customized method should be applied to each of the detected clusters. Second, in the runtime phase of our framework, the results provided from the offline phase are used to perform computationally cheap prediction. The experimental results show that our approach has a prediction error below 12% and provides an error reduction of more than 17% in comparison with a straightforward method.

Place, publisher, year, edition, pages
IEEE , 2017. p. 524-528
Keyword [en]
Clustering; interarrival time; machine learning; prediction; resource manager; time series analysis
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-147213DOI: 10.1109/DSD.2017.42ISI: 000427097100076ISBN: 978-1-5386-2146-2 OAI: oai:DiVA.org:liu-147213DiVA, id: diva2:1197315
Conference
20th Euromicro Conference on Digital System Design (DSD)
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-04-12

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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