Optimized Workload Allocation in Vehicular Edge Computing: A Sequential Game Approach
With the development of Vehicle-to-Everything (V2X) communication technologies, Vehicular Edge Computing (VEC) is utilized to speed up the running of vehicular computation workload by deploying VEC servers in close proximity to vehicular terminals. Due to resource limitation of VEC servers, VEC servers are unable to perform a large number of vehicular computation workloads. To improve the performance of VEC servers, we propose a new workload allocation framework where vehicular terminals are divided into Resource Provision Terminals (RPTs) and Resource Demand Terminals (RDTs). In this framework, we design an optimized workload allocation strategy through a sequential Stackelberg game. With the sequential Stackelberg game, a VEC server, RDTs, and RPTs achieve an efficient coordination of the workload allocation. The sequential Stackelberg game is proven to reach two sequential Nash Equilibriums. The simulation results validate the efficiency of the optimized workload allocation strategy.
KeywordsVehicular edge computing Workload allocation Sequential Stackelberg game
This work was supported in part by programs of NSFC under Grant nos. 61422201, 61370159 and U1301255, U1501251, the Science and Technology Program of Guangdong Province under Grant no. 2015B010129001, Special-Support Project of Guangdong Province under grant no. 2014TQ01X100, High Education Excellent Young Teacher Program of Guangdong Province under grant no. YQ2013057, Science and Technology Program of Guangzhou under grant no. 2014J2200097.
- 3.Zhang, K., Mao, Y., Leng, S., Vinel, A., Zhang, Y.: Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks. In: 2016 8th International Workshop on Resilient Networks Design and Modeling (RNDM), pp. 288–294. IEEE (2016)Google Scholar
- 5.Yu, R., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y., Tsang, D.: Decentralized and optimal resource cooperation in geo-distributed mobile cloud computing. IEEE Trans. Emerg. Top. Comput. (2017)Google Scholar
- 8.Wen, Y., Zhang, W., Luo, H.: Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones. In: INFOCOM, 2012 Proceedings IEEE, pp. 2716–2720. IEEE (2012)Google Scholar
- 9.Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)Google Scholar
- 12.Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, R., Han, Z.: Fog computing in multi-tier data center networks: a hierarchical game approach. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)Google Scholar
- 13.Lee, J., Guo, J., Choi, J.K., Zukerman, M.: Distributed energy trading in microgrids: a game-theoretic model and its equilibrium analysis. IEEE Trans. Ind. Electron. 62(6), 3524–3533 (2015)Google Scholar