Delay-Aware and Energy-Efficient Task Offloading Based on Adaptive Large Neighborhood Search

Mobile edge computing boosts the application performance on mobile devices by collaborating with cloud platforms. This paper studies the task offloading and computing resource allocation problem in a multibase, multiserver, and multiuser scenario subject to resource constraints. The goal is to maximize the users' task offloading utility, including improvements in task completion time, energy consumption, and communication cost. The addressed problem is formulated as a mixed integer nonlinear programming (MINLP) model. In this paper, we decompose the MINLP and the optimal computing resource allocation policy under a deterministic offloading strategy obtained by the Karush-Kuhn-Tucker conditions. Then, a hybrid adaptive large neighborhood search (HALNS) algorithm is proposed to conduct task offloading. The adaptive large neighborhood search and the variable neighborhood descent stages are jointly employed in HALNS. The proposed algorithm, an improved simulated annealing algorithm, and a modified variable neighborhood search algorithm are executed to evaluate their performances. Digital experimental results show that our proposed algorithm achieves higher system utility, lower delays, and less energy consumption.