AuthorsY. Dai, D. Xu, S. Maharjan and Y. Zhang
TitleJoint Load Balancing and Offloading in Vehicular Edge Computing and Networks
AfilliationCommunication Systems
Project(s)Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications
Publication TypeJournal Article
Year of Publication2018
JournalIEEE Internet of Things Journal
Date Published10/2018

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 multi-user multi-server VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer non-linear 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 (JSCO) algorithm compared to the benchmark solutions.

Citation Key26441