Job Scheduling and Optimization of Job-Worker Assignments in Distributed Computing Environments

Job Scheduling and Optimization of Job-Worker Assignments in Distributed Computing Environments
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© 2025 by IJETT Journal
Volume-73 Issue-7
Year of Publication : 2025
Author : Regis Donald HONTINFINDE, Ariel AMOYEDJI, Mahugnon Geraud AZEHOUN-PAZOU, Marcos Thyrbus VITOULEY, Christian Djidjoho AKOWANOU
DOI : 10.14445/22315381/IJETT-V73I7P101

How to Cite?
Regis Donald HONTINFINDE, Ariel AMOYEDJI, Mahugnon Geraud AZEHOUN-PAZOU, Marcos Thyrbus VITOULEY, Christian Djidjoho AKOWANOU, "Job Scheduling and Optimization of Job-Worker Assignments in Distributed Computing Environments," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.1-11, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P101

Abstract
A genetic algorithm is a metaheuristic inspired by the process of natural selection, which belongs to the large class of evolutionary algorithms. This makes it a good candidate for the development of new algorithms to solve optimization problems. In this study, we investigated the User-PC computing (UPC) system by proposing a genetic algorithm to assign jobs to workers to minimize the response time. To evaluate the effectiveness of the proposed genetic algorithm, we conducted experiments involving the execution of 72 jobs on the UPC system, using six worker PCs with varying numbers of threads and processor cores. We evaluated the algorithm in two distinct scenarios: static and dynamic job scheduling. In the static scheduling scenario, the algorithm assigns all available jobs to workers in a manner that minimizes the overall response time. The dynamic assignment scenario assigns newly arrived jobs to workers as they become available. The results demonstrate that the proposed genetic algorithm achieved a 26.4% reduction in response time compared to the random multiplestart local search (DRMSLS) algorithm. The results of this research have the potential to improve the performance of Next-Generation Networks (NGNs) in the telecommunications sector.

Keywords
Genetic Algorithm, Grid computing, Telecommunications networks, Thread job scheduling, UPC distributed computing.

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