Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks

TLDR

Computationally intensive, delay‑sensitive on‑vehicle applications challenge vehicle resources, and while vehicular edge computing can offload tasks to ubiquitous servers, naïve schemes that send all tasks to a single server risk overload and limit performance gains. The study proposes integrating load balancing with offloading to optimize resource allocation in a multi‑user, multi‑server VEC system. The authors formulate the joint load‑balancing and offloading problem as a mixed‑integer nonlinear program that maximizes system utility under IEEE 802.11p constraints, then decompose it into two subproblems and devise a low‑complexity algorithm for server selection, offloading ratios, and computation resource allocation. Numerical results show the algorithm converges quickly and outperforms benchmark solutions, demonstrating superior performance of the joint optimal VEC server selection and offloading strategy.

Abstract

The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions.