An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

Vehicular ad hoc networks are expected to significantly improve traffic safety and transportation efficiency while providing a comfortable driving experience. However, available communication, storage, and computation resources of the connected vehicles are not well utilized to meet the service requirements of intelligent transportation systems. Vehicular cloud computing (VCC) is a promising approach that makes use of the advantages of cloud computing and applies them to vehicular networks. In this paper, we propose an optimal computation resource allocation scheme to maximize the total long-term-expected reward of the VCC system. The system reward is derived by taking into account both the income and cost of the VCC system as well as the variability feature of available resources. Then, the optimization problem is formulated as an infinite horizon semi-Markov decision process (SMDP) with the defined state space, action space, reward model, and transition probability distribution of the VCC system. We utilize the iteration algorithm to develop the optimal scheme that describes which action has to be taken under a certain state. Numerical results demonstrate that the significant performance gain can be obtained by the SMDP-based scheme within the acceptable complexity.