Title: A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption

Authors: Hassun Vakilian Zand; Mohsen Raji; Hossein Pedram; Hossein Heidari SharifAbadi

Addresses: Department of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran ' School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran ' Department of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran ' Department of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran

Abstract: Energy consumption has been always an important issue in multicore processors which are getting more and more popular in embedded systems. In this paper, we propose an energy-aware task scheduling approach taking advantages of heuristic algorithms based on genetic algorithm. The proposed approach includes both static and dynamic scheduling schemes. The task scheduling is modelled as a genetic algorithm problem which is mainly used when the tasks are ready before run-time; i.e., static task scheduling. The tasks which arrive after beginning task execution are dynamically scheduled using a proposed heuristic algorithm in combination with the genetic algorithm. The experimental results show that the proposed algorithm achieves more energy efficiency in both static and dynamic task scheduling for multicore processors as compared with similar energy-aware scheduling methods.

Keywords: multicore processor; task scheduling; energy consumption; genetic algorithm; evolutionary algorithm; embedded system; heuristic algorithm.

DOI: 10.1504/IJES.2020.109957

International Journal of Embedded Systems, 2020 Vol.13 No.3, pp.264 - 273

Received: 26 Nov 2018
Accepted: 16 Jun 2019

Published online: 11 May 2020 *

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