EHGA: A Genetic Algorithm Based Approach for Scheduling Tasks on Distributed Edge-Cloud Infrastructures

Abstract

Due to cloud computing's limitations, edge computing has emerged to address computation-intensive and time-sensitive applications. In edge computing, users can offload their tasks to edge servers. However, the edge servers' resources are limited, making task scheduling everything but easy. In this paper, we formulate the scheduling of tasks between the user equipment, the edge, and the cloud as a Mixed-Integer Linear Programming (MILP) problem that aims to minimize the total system delay. To solve this MILP problem, we propose an Enhanced Healed Genetic Algorithm solution (EHGA). The results with EHGA are compared with those of CPLEX and a few heuristics previously proposed by us. The results indicate that EHGA is more accurate and reliable than the heuristics and Quicker than CPLEX at solving the MILP problem.