jMetal and MFHS collaboration for task scheduling optimization in heterogeneous distributed system

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Abdelhamid Khiat


Task scheduling in distributed computing architectures has attracted considerable research interest, leading to the development of numerous algorithms aiming to approach optimal solutions. However, most of these algorithms remain confined to simulation environments and are rarely applied in real-world settings. In a previous study, we introduced the MFHS framework, which facilitates the transition of scheduling algorithms from simulation to practical deployment. Unfortunately, MFHS currently offers a limited selection of scheduling heuristics. In this work, we address this limitation by presenting the MFHS_jMetal framework, integrating the extensive task scheduling algorithms available in the well-established jMetal framework. Our implementation demonstrates the successful expansion of available scheduling algorithms while preserving the core characteristics of MFHS, bridging the gap between theoretical models and real-world deployment.


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Khiat, A. 2024. jMetal and MFHS collaboration for task scheduling optimization in heterogeneous distributed system. Journal of Innovative Applied Mathematics and Computational Sciences. 3, 2 (Jan. 2024), 162–172. DOI:
Research Articles


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