Delay-sensitive tasks offloading in multi-access edge computing

Abstract

In recent years, edge computing has made up for the shortcomings of cloud computing’s centralized data processing. It migrates computation to edge devices close to users, which reduces the user’s transmission time, calculation time, propagation time, and other times, so it meets the request of delay-sensitive tasks. In this multi-access edge computing system, edge devices are divided into different cooperation spaces. Edge devices in the same cooperation space collaborate with others through sharing resources. Tasks are divided into multiple computations, each of which can be executed on different edge devices. A task offloading problem is formulated to minimize the average delay of all tasks in multi-access edge computing system. An algorithm based on ant colony optimization is proposed in order to find the best solution for task offloading. To make better decisions in the first iteration, the pheromone matrix is initialized considering two factors of base station load and distance between users and base stations. According to the relationship between fitness function and the global optimal value or local optimal value, the values of pheromones are updated dynamically. A large number of experiments show that our algorithm has better performance.