Journal of Smart Computing ISSN 2979-8752 · Biannual
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Research Article · Vol. 2, No. 1 (2026)

A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling in Cloud-Edge Computing Environments

Rahil Hafezian , Mohammad Fatemi*

Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran · Industrial Engineering and Management, University of Turku, Turku, Finland

* Corresponding author: mohammad.fatemi@utu.fi

Abstract The growth of latency-sensitive applications at the network edge has made task scheduling a central problem in cloud-edge computing, where energy consumption, response time, and resource utilization must be balanced at the same time. Scheduling in heterogeneous, distributed environments is NP-hard: exact methods do not scale, and single metaheuristics often converge prematurely or exploit the search space poorly. This paper proposes a hybrid metaheuristic that combines the global exploration of the Grey Wolf Optimizer with the focused exploitation of the Whale Optimization Algorithm and a memetic local search. A weighted multi-objective fitness jointly minimizes energy and makespan while maximizing resource utilization. The scheduler is defined within a layered cloud-edge model with a dynamic voltage and frequency scaling energy formulation. The algorithm is specified formally and in pseudocode, and evaluated in an illustrative discrete-event simulation against four baselines on workloads ranging from 100 to 600 tasks. The synthetic results show lower energy and makespan and higher utilization than the baselines, which supports hybrid metaheuristics as a practical approach to energy-aware cloud-edge scheduling.

Keywords: Task scheduling, Energy-aware computing, cloud-Edge computing, hybrid Metaheuristic, Grey Wolf Optimizer

DOI
10.5281/zenodo.23257724
Pages
11-21
Published
20 Feb 2026
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To cite this article: Hafezian, R., & Fatemi, M. (2026). A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling in Cloud-Edge Computing Environments. Journal of Smart Computing , 2(1) , 11-21. https://doi.org/10.5281/zenodo.23257724

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