A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption. (11th May 2020)

• Record Type: Journal Article
• Authors: Zand, Hassun Vakilian; Raji, Mohsen; Pedram, Hossein; SharifAbadi, Hossein Heidari
• Is Part Of: International journal of embedded systems. Volume 13:Number 3(2020)
• Page Start: 264
• Page End: 273
• Publication Date: 2020-05-11
• Subjects: multicore processor -- task scheduling -- energy consumption -- genetic algorithm -- evolutionary algorithm -- embedded system -- heuristic algorithm; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

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.