An Enhanced Grey Wolf Optimization Algorithm for Efficient Task Scheduling in Mobile Edge Computing

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