Opposition Strategy with Harris Hawk Algorithm for Task Scheduling in Edge Computing

Edge computing (EC) nodes undertake an increasing number of tasks with rise of business density. However, unreasonable edge computing resource allocation suffers from high energy consumption and resource waste. This paper, Opposition-Based Learning with Harris Hawk Algorithm (OHHO) using task scheduling in EC, distributed involves complex and dynamic environment with changing tasks and resources availability. The HHO has mechanisms to maintain exploration and exploitation, reduce high dimensionality in search space where algorithm avoids to fall in local optima. OBL helps in this dynamic context by ensuring suboptimal solutions are quickly discarded and replaced with their opposites, which allows algorithm to adapt better to recent situation. This is adaptability is crucial in edge computing, where tasks and resources are constantly fluctuating. The resource allocation involved Dynamic Priority Task Scheduling Algorithm (DPTSA) helps in balancing system load by distributing tasks based on their priority and availability of resources. The proposed OHHO algorithm achieves high performance such energy 0.1mJ, Makespan of 1s and Delay of 45ms respectively when compared to existing techniques Deep Q Network (DQN) and Particle Swarm Optimization (PSO) - Grey Wolf Optimizer (GWO) approach.