Hybrid Heuristic Task Offloading Algorithm with Edge Caching Mechanism

Abstract

Edge computing is considered one of the key technologies that can solve the problem of insufficient computing resources in large-scale networks in the future due to its short physical distance from users and fast response speed.In the multi-in multi-out edge computing environment, some services can be cached at the edge nodes to reduce the execution time of user-requested tasks.However, previous studies assumed that the edge nodes have infinite cache space or ignored the fact that the current cache list and cache replacement mechanism impact task offloading.This may invalidate the offloading decision and delay the execution of tasks.Therefore, a hybrid heuristic algorithm called IPSO_GA based on integer coding is proposed for the edge computing environment with the caching mechanism.The task offloading problem is modeled as a mixed-integer nonlinear programming problem.Combined with particle swarm optimization and Genetic Algorithm(GA), each particle constantly searches for an optimal solution through mating and variational operations, which can search for task offloading decisions within a reasonable time complexity.Experimental results show that the IPSO_GA algorithm reduces task execution time by approximately 58% to 298% compared with classical algorithms such as the Random algorithm, Greedy algorithm, Even algorithm, and the current newer algorithms, and can be applied to edge computing environments with a large number of devices and intensive computation.