International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 73 | Issue 11 | Year 2025 | Article Id. IJETT-V73I11P104 | DOI : https://doi.org/10.14445/22315381/IJETT-V73I11P104

Heuristic-Based Workload Scheduling Approaches in Edge-Cloud Environments: A Review

Hasnae NOUHAS, Abdessamad BELANGOUR, Mahmoud NASSAR

Received Revised Accepted Published
01 Jul 2025 29 Oct 2025 03 Nov 2025 25 Nov 2025

Citation :

Hasnae NOUHAS, Abdessamad BELANGOUR, Mahmoud NASSAR, "Heuristic-Based Workload Scheduling Approaches in Edge-Cloud Environments: A Review," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 11, pp. 39-50, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I11P104

Abstract

Edge-cloud computing architecture has become appropriate and promising to satisfy the performance requirements of resource-intensive and latency-critical applications. Effective scheduling of workload serves the purpose of exploiting the distributed, heterogeneous, and dynamic characteristics of these environments. Among the developed approaches, heuristic-based scheduling methods are important due to the low level of complexity and practical merit they present in real-time and resource-limited environments. Heuristic-based workload scheduling methods are the center of focus of this paper. Present methods are classified into simple heuristics, metaheuristics, and hybrid schemes, and surveyed on prominent examples of HEFT, ACO, PSO, Min-Min/Max-Min, and Greedy Resource-Aware algorithms. Each of these is put through the prism of the scheduling objectives, benefits, and their tradeoff on various executed metrics like latency, energy efficiency, and flexibility. Though these strategies are beneficial, the issues associated with their usability for dynamic applications and multi-objective scenarios are prominent. Important research gaps are listed along with proposed future works, including adaptations, energy-oriented, and lightweight scheduling models. Of even higher value, it is notable to recognize the growing interest in the application of AI-based schemes, which have the potential to enhance heuristic-based scheduling once integrated into hybrid systems. This survey aspires to present the convenient go-to thesis of the researcher tackling the challenge of creating a productive workload scheduling design in the edge-cloud infrastructures.

Keywords

Cloud computing, Edge computing, Workload scheduling, Heuristic Algorithms, Metaheuristic Algorithms.

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