Abderrazak Farchane

Work place: Laboratory of Innovation in Mathematics, Applications, and Information Technology, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, 23000, Morocco

E-mail: a.farchane@gmail.com

Website:

Research Interests: Coding Theory

Biography

Abderrazak Farchane received his B.Sc. in Computer Science and Engineering in June 2001 and M.Sc. in Computer Science and Telecommunication from the University of Mohammed V Agdal, Rabat, Morocco, in 2003. He obtained his Ph.D. in Computer Science and Engineering at ENSIAS, Rabat, Morocco. He is currently an Associate Professor of Computer Science in the Polydisciplinary Faculty, at Sultan Moulay Slimane University, Morocco. His areas of interest are Information Coding Theory, Cryptography, and Security.

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

By Marouane Myyara Oussama Lagnfdi Anouar Darif Abderrazak Farchane

DOI: https://doi.org/10.5815/ijcnis.2024.04.01, Pub. Date: 8 Aug. 2024

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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