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Runtime Resource Management with Workload Prediction
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Gears of Leo AB, Sweden.
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.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Modern embedded platforms need sophisticated resource managers in order to utilize the heterogeneous computational resources efficiently. Moreover, such platforms are exposed to fluctuating workloads unpredictable at design time. In such a context, predicting the incoming workload might improve the efficiency of resource management. But is this true? And, if yes, how significant is this improvement? How accurate does the prediction need to be in order to improve decisions instead of doing harm? By proposing a prediction-based resource manager aimed at minimizing energy consumption while meeting task deadlines and by running extensive experiments, we try to answer the above questions.

Place, publisher, year, edition, pages
ACM Digital Library, 2019.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-160935DOI: 10.1145/3316781.3317902ISI: 000482058200169Scopus ID: 2-s2.0-85067784119ISBN: 978-1-4503-6725-7 (print)OAI: oai:DiVA.org:liu-160935DiVA, id: diva2:1361177
Conference
56th Annual Design Automation Conference 2019, Las Vegas, NV, USA, June 02 - 06, 2019
Available from: 2019-10-15 Created: 2019-10-15 Last updated: 2019-10-25Bibliographically approved

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Niknafs, Mina

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Language
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  • nn-NB
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