|Authors||Y. Dai, D. Xu, S. Maharjan and Y. Zhang|
|Title||Joint Offloading and Resource Allocation in Vehicular Edge Computing and Networks|
|Project(s)||Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications|
|Publication Type||Proceedings, refereed|
|Year of Publication||2018|
|Conference Name||IEEE Globecom 2018|
The emergence of computation intensive on-vehicle applications poses a significant challenge to provide the required computation capacity and maintain high performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a high potential to improve vehicular services by offloading computation-intensive tasks to the VEC servers. Nevertheless, as the computation resource of each VEC server is limited, offloading may not be efficient if all vehicles select the same VEC server to offload their tasks. To address this problem, in this paper, we propose offloading with resource allocation. We incorporate the communication and computation to derive the task processing delay. We formulate the problem as a system utility maximization problem, and then develop a low-complexity algorithm to jointly optimize offloading decision and resource allocation. Numerical results demonstrate the superior performance of our Joint Optimization of Selection and Computation (JOSC) algorithm compared to state of the art solutions.