Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization

The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes.