Quality of Experience Improvement and Service Time Optimization through Dynamic Computation Offloading Algorithms in Multi-access Edge Computing Networks

Автор: Marouane Myyara, Oussama Lagnfdi, Anouar Darif, Abderrazak Farchane

Журнал: International Journal of Computer Network and Information Security @ijcnis

Статья в выпуске: 4 vol.16, 2024 года.

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Multi-access Edge Computing optimizes computation in proximity to smart mobile devices, addressing the limitations of devices with insufficient capabilities. In scenarios featuring multiple compute-intensive and delay-sensitive applications, computation offloading becomes essential. The objective of this research is to enhance user experience, minimize service time, and balance workloads while optimizing computation offloading and resource utilization. In this study, we introduce dynamic computation offloading algorithms that concurrently minimize service time and maximize the quality of experience. These algorithms take into account task and resource characteristics to determine the optimal execution location based on evaluated metrics. To assess the positive impact of the proposed algorithms, we employed the Edgecloudsim simulator, offering a realistic assessment of a Multi-access Edge Computing system. Simulation results showcase the superiority of our dynamic computation offloading algorithm compared to alternatives, achieving enhanced quality of experience and minimal service time. The findings underscore the effectiveness of the proposed algorithm and its potential to enhance mobile application performance. The comprehensive evaluation provides insights into the robustness and practical applicability of the proposed approach, positioning it as a valuable solution in the context of MEC networks. This research contributes to the ongoing efforts in advancing computation offloading strategies for improved performance in edge computing environments.

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Computation Offloading, Quality of Experience, Service Time, Workload Orchestration, Multi-access Edge Computing Network

Короткий адрес: https://sciup.org/15019292

IDR: 15019292 | DOI: 10.5815/ijcnis.2024.04.01

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