Energy-efficient Task Offloading Using Hybrid Particle Swarm Optimization with Genetic Operations in Smart Edge

Abstract

Wireless capacities and battery energy of smart mobile devices (SMDs) are constrained, and therefore, only limited tasks of applications can be executed in SMDs. To solve this problem, some computation tasks in SMDs can be partially offloaded to edge servers with larger processing capacities. Nonetheless, communication latency is caused for offloaded tasks because of channel bandwidth limits between SMDs and edge servers due to the task offloading. This work designs an energy-efficient task offloading approach to achieve energy consumption minimization for edge servers and SMDs by comprehensively specifying a task offloading ratio, SMDs’ processing speeds and transmission power, and channel bandwidth allocation. Specifically, a mixed-integer nonlinear programming problem is formulated for a smart edge provider. Then, it is solved by using a hybrid particle swarm optimization algorithm with genetic operations for obtaining a close-to-optimal task offloading strategy for edge servers and SMDs. It is evaluated by adopting real-life data from Google production cluster, and simulation results demonstrate that it achieves less consumption of energy for the smart edge provider in a faster way compared with its two state-of-the-art benchmark algorithms.