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Article

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Department of Computer and Systems, Faculty of Engineering, Helwan University, Cairo 11111, Egypt
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Author to whom correspondence should be addressed.
Received: 16 December 2020 / Revised: 28 December 2020 / Accepted: 30 December 2020 / Published: 8 January 2021
(This article belongs to the Special Issue Real-Time Systems in Emerging IoT-Embedded Applications)
Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability. View Full-Text
Keywords: dynamic voltage/frequency scaling; energy-aware partitioning; multi-core real-time systems; shared resources; task allocating and scheduling dynamic voltage/frequency scaling; energy-aware partitioning; multi-core real-time systems; shared resources; task allocating and scheduling
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MDPI and ACS Style

El Sayed, M.A.; Saad, E.S.M.; Aly, R.F.; Habashy, S.M. Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms. Computers 2021, 10, 10. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010010

AMA Style

El Sayed MA, Saad ESM, Aly RF, Habashy SM. Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms. Computers. 2021; 10(1):10. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010010

Chicago/Turabian Style

El Sayed, Manal A., El S.M. Saad, Rasha F. Aly, and Shahira M. Habashy 2021. "Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms" Computers 10, no. 1: 10. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010010

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