Energy efficient scheduling of real-time tasks on multicore processors

Euiseong Seo, Jinkyu Jeong, Seonyeong Park, Joonwon Lee

    Research output: Contribution to journalArticlepeer-review


    Multicore processors deliver a higher throughput at lower power consumption than unicore pro- cessors. In the near future, they will thus be widely used in mobile real-time systems. There have been many research on energy-efficient scheduling of real-time tasks using DVS. These approaches must be modified for multicore processors, however, since normally all the cores in a chip must run at the same performance level. Thus blindly adopting existing DVS algorithms which do not consider the restriction will result in a waste of energy. This article suggests Dynamic Repartitioning algorithm based on existing partitioning approaches of multiprocessor systems. The algorithm dynamically balances the task loads of multiple cores to optimize power consumption during execution. We also suggest Dynamic Core Scaling algorithm which adjusts the number of active cores to reduce leakage power consumption under low load conditions. Simulation results show that Dynamic Repartitioning can produce energy savings of about 8% even with the best energy-efficient partitioning algorithm. The results also show that Dynamic Core Scaling can reduce energy consumption by about 26% under low load conditions.

    Original languageEnglish
    Pages (from-to)1540-1552
    Number of pages13
    JournalIEEE Transactions on Parallel and Distributed Systems
    Issue number11
    Publication statusPublished - 2008

    Bibliographical note

    Funding Information:
    This research was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-357-D00199) and also by the Ministry of Knowledge Economy of Korea under the Information Technology Research Center (ITRC) Support program supervised by the Institute of Information Technology Advancement (IITA) (IITA-2008-C1090-0801-0020).

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Hardware and Architecture
    • Computational Theory and Mathematics


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