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Title |
Systematic Review of Metaheuristic Task Scheduling Algorithms on Edge Computing |
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Author |
Jafar Aminu, Rohaya Latip, Zurina Mohd Hanafi, Shafinah Kamarudin, Danlami Gabi |
| Citation |
Vol. 25 No. 12 pp. 176-200 |
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Abstract |
The proliferation of data sensors and mobile devices generates extensive amounts of data that must be processed in real-time. This requirement is fulfilled through nearby edge servers, though effective task scheduling is crucial to ensure timely task completion within resource limitations. With the increase in data volumes, timely task completion in MEC becomes an uphill task. Because of the ample solution space, task scheduling in edge computing is an NP-hard problem. While polynomial-time algorithms cannot provide optimal solutions, metaheuristic-based techniques have proven effective in near-optimal solutions within a short time frame. This paper presents a systematic review and comparative analysis of key metaheuristic algorithms, Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Ant Colony Optimization (ACO) in the context of task scheduling for MEC. The study evaluates these algorithms against key quality of service (QoS) parameters such as energy consumption, completion time, execution time, delay, and cost. A total of 103 studies were analyzed, and it has been found that though these metaheuristic techniques bring many advantages, key issues are still to be considered. Task relocation during scheduling needs much attention to be made more feasible. Furthermore, most of the current techniques need more consideration of network bandwidth, which leads to possible transmission breakdowns. In the end, the paper concludes that this could be a direction for future research, considering these factors and studying the integration of metaheuristics with AI techniques to develop more robust and adaptive scheduling strategies for MEC environments. |
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Keywords |
Mobile Edge Computing, Task scheduling Metaheuristic Approaches, Quality of service |
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