Latency-Energy Joint Optimization for Task Offloading and Resource Allocation in MEC-Assisted Vehicular Networks
Yuliang Cong, Ke Xue, Cong Wang, W. Y. Sun, Shuxian Sun, Fengye Hu
Abstract
In this article, we study the task offloading problem on mobile edge in vehicular networks. Specifically, we take computational resource constraints into consideration, and aim to simultaneously reduce latency and energy consumption. For this purpose, we establish an offloading model that consists of local edge computing resources, edge server resources of both macro and subsidiary base stations, as well as cloud computing server resources. Each task can be offloaded through one of five strategies, and is evaluated via a loss function determined by its latency and energy consumption. Based on this model, our goal is to solve a mixed-integer non-linear optimization problem (MINLP) whose objective function is the weighted sum of the task-specific loss functions. To address this optimization problem, we split it into two sub-problems, referred to as resource allocation and offloading strategy. We develop a method based on Block Coordinate Descent technique combining convex optimization and Gray Wolf algorithm (BCD-CONGW) that alternatively solves the two sub-problems, until convergence. The former sub-problem is convex and can be solved in polynomial time, whereas the latter is non-convex and hence NP-hard. For the latter, we relax discrete variables and employ Gray Wolf algorithm with elite strategy to approximate its optimal point. By numerical evaluations, we show that our method outperforms existent methods in terms of latency and energy consumption.