International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 186 - Issue 56 |
Published: December 2024 |
Authors: Jafar Aminu, Rohaya Latip, Zurina Mohd Hanafi, Shafinah Kamarudin, Bashar Umar Kangiwa, Ayuba Liman |
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Jafar Aminu, Rohaya Latip, Zurina Mohd Hanafi, Shafinah Kamarudin, Bashar Umar Kangiwa, Ayuba Liman . An Enhanced Grey Wolf Optimization Algorithm for Efficient Task Scheduling in Mobile Edge Computing. International Journal of Computer Applications. 186, 56 (December 2024), 39-44. DOI=10.5120/ijca2024924287
@article{ 10.5120/ijca2024924287, author = { Jafar Aminu,Rohaya Latip,Zurina Mohd Hanafi,Shafinah Kamarudin,Bashar Umar Kangiwa,Ayuba Liman }, title = { An Enhanced Grey Wolf Optimization Algorithm for Efficient Task Scheduling in Mobile Edge Computing }, journal = { International Journal of Computer Applications }, year = { 2024 }, volume = { 186 }, number = { 56 }, pages = { 39-44 }, doi = { 10.5120/ijca2024924287 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2024 %A Jafar Aminu %A Rohaya Latip %A Zurina Mohd Hanafi %A Shafinah Kamarudin %A Bashar Umar Kangiwa %A Ayuba Liman %T An Enhanced Grey Wolf Optimization Algorithm for Efficient Task Scheduling in Mobile Edge Computing%T %J International Journal of Computer Applications %V 186 %N 56 %P 39-44 %R 10.5120/ijca2024924287 %I Foundation of Computer Science (FCS), NY, USA
Mobile edge computing (MEC) is a fundamental paradigm that brings computational resources closer to end users, minimizing latency and improving performance for real-time applications. Task scheduling optimization is generally a significant difficulty in MEC systems because of the dynamic nature of edge servers, limited processing resources, and energy restrictions. This leads to several problems, such as high energy consumption, makespan, extended task execution durations, and inefficient resource utilization. An enhanced grey wolf optimization method that introduces novel strategies to balance the exploration and exploitation processes more successfully will be used in this study to address these problems. The suggested EGWO algorithm handles the dynamic task allocation for maximum usage of resources, which minimizes makespan and energy consumption. We undertake comprehensive simulations for different workloads and show that EGWO consistently performs better than state-of-the-art techniques like WOA, PSO, and RFOAOA. EGWO leads to significant improvements in energy efficiency and makespan. It is, therefore a reliable and scalable solution for scheduling tasks in the MEC environment