Task Offloading Method for Energy Consumption Optimization in Ultra-Dense Edge Computing Network

Abstract

The edge server deployed in a conventional network architecture exhibits difficulty meeting the requirements of large-scale user equipment access and communication quality.To increase network capacity and improve spectrum utilization, dense base station deployment is combined with Ultra-Dense Network(UDN) to develop a task offloading optimization model for an ultra-dense edge computing network.The reasons for changes in channel status, the dynamic requirements of mobile devices, and the limitations of servers and spectrum resources pose challenges for offloading.A genetic algorithm based on an Adaptive Genetic Algorithm with Simulated Annealing (AGASA)'s task offloading method optimizes the energy consumption of task offloading while meeting the task deadline by combining the task type and the computing power of the server and considering the influence of channel state changes, mobile device dynamic requirements, and interference constraints on the offloading strategy.Meanwhile, to improve upload power, this study solves the power control problem with the golden section algorithm, saving transmission energy consumption.The experimental results demonstrate that when the channel state changes, the proposed task offloading strategy ensures communication quality and computational efficiency.It can meet deadline constraints while reducing its offloading energy consumption by 15.56% when compared to the hybrid genetic particle swarm algorithm(GAPSO).