Hussain, Hameed, Zakarya, Muhammad, Ali, Ahmad, Khan, Ayaz Ali, Chalak Qazani, Mohammad Reza, Al-Bahri, Mahmood, and Haleem, Muhammad (2024) Energy efficient real-time tasks scheduling on high-performance edge-computing systems using genetic algorithm. IEEE Access, 12. pp. 54879-54892.

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Abstract

With an increase in the number of processing cores or systems, the high-performance edge-computing system’s power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems is to execute these systems at the slowest feasible speed so that the job’s deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper, we propose a scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks’ deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (~18.56%) and response times (~2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

Item ID:	86714
Item Type:	Article (Research - C1)
ISSN:	2169-3536
Keywords:	Genetic algorithm, edge-computing, multi-core, real-time systems, feasibility analysis, HPC.
Copyright Information:	© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Date Deposited:	14 Oct 2025 03:35
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460699 Distributed computing and systems software not elsewhere classified @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460606 Energy-efficient computing @ 20%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 50% 17 ENERGY > 1701 Energy efficiency > 170199 Energy efficiency not elsewhere classified @ 40% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 10%
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