Metaheuristics Based Energy Efficient Task Scheduling Scheme for Cyber-Physical Systems Environment

Anwer Mustafa Hilal (), Aisha Hassan Abdalla Hashim, Marwa Obayya, Abdulbaset Gaddah, Abdullah Mohamed, Ishfaq Yaseen, Mohammed Rizwanullah and Abu Sarwar Zamani
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Anwer Mustafa Hilal: Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Aisha Hassan Abdalla Hashim: Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Marwa Obayya: Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdulbaset Gaddah: Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi Arabia
Abdullah Mohamed: Research Centre, Future University in Egypt, New Cairo 11845, Egypt
Ishfaq Yaseen: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Mohammed Rizwanullah: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Abu Sarwar Zamani: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia

Sustainability, 2022, vol. 14, issue 24, 1-17

Abstract: The widespread applicability of cyber-physical systems (CPS) necessitates efficient schemes to optimize the performance of both computing units and physical plant. Task scheduling (TS) in CPS is of vital importance to enhance resource usage and system efficiency. Traditional task schedulers in embedded real-time systems are unable to fulfill the performance requirements of CPS because of the task diversity and system heterogeneities. In this study, we designed a new artificial rabbit optimization enabled energy-efficient task-scheduling scheme (ARO-EETSS) for the CPS environment. The presented ARO-EETSS technique is based on the natural survival practices of rabbits, comprising detour foraging and arbitrary hiding. In the presented ARO-EETSS technique, the TS process is performed via the allocation of n autonomous tasks to m different resources. In addition, the objective function is based on the reduction of task completion time and the effective utilization of resources. In order to demonstrate the higher performance of the ARO-EETSS system, a sequence of simulations was implemented. The comparison study underlined the improved performance of the ARO-EETSS system in terms of different measures.

Keywords: task scheduling; Internet of Things; cyber physical system; artificial rabbit optimization; high performance computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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