Open Access

ARTICLE

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

J. Jean Justus¹, U. Sakthi², K. Priyadarshini³, B. Thiyaneswaran⁴, Masoud Alajmi⁵, Marwa Obayya⁶, Manar Ahmed Hamza^7,*

1 Department of Computer Science & Engineering, St. Joseph’s College of Engineering, Chennai, 600119, India
2 Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602 105, India
3 Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
4 Department of Electronics and Communication Engineering, Sona College of Technology, Salem, 636 005, India
5 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
6 Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
7 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computer Systems Science and Engineering 2023, 44(1), 205-219. https://doi.org/10.32604/csse.2023.025256

Received 17 November 2021; Accepted 20 December 2021; Issue published 01 June 2022

Abstract

The developments of multi-core systems (MCS) have considerably improved the existing technologies in the field of computer architecture. The MCS comprises several processors that are heterogeneous for resource capacities, working environments, topologies, and so on. The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling. At the same time, the task scheduling process is yet to be explored in the multi-core systems. This paper presents a new hybrid genetic algorithm (GA) with a krill herd (KH) based energy-efficient scheduling technique for multi-core systems (GAKH-SMCS). The goal of the GAKH-SMCS technique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation. The GAKH-SMCS model involves a multi-objective fitness function using four parameters such as makespan, processor utilization, speedup, and energy consumption to schedule tasks proficiently. The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset. The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan, processor utilization, speedup, and energy consumption. The overall simulation results depicted that the presented GAKH-SMCS model achieves energy efficiency by optimal task scheduling process in MCS.

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Keywords

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