Abstract
With the emergence and development of the Internet of Vehicles (IoV), higher demands are placed on the response speed and ultra-low delay of the vehicle. Cloud computing services are not friendly to reducing latency and response time. Mobile Edge Computing (MEC) is a promising solution to this problem. In this paper, we introduce MEC into the IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FN), data service agents (DSA), and cars. We proposed a dynamic service area partitioning algorithm that enables the DSA to adjust the service area and provide a more efficient service for the vehicle. A resource allocation framework based on Stackelberg game model is proposed to analyze the pricing problem of FN and data resource strategy of DSA. We use the distributed iterative algorithm to solve the problem of game equilibrium. Our proposed resource management framework is finally verified by numerical results, which show that the allocation efficiency of FN resources among the cars is ensured, and we also get a subgame perfect nash equilibrium.
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References
Hu, Q., Wu, C., Zhao, X., Chen, X., Yoshinaga, T.: Vehicular multi-access edge computing with licensed Sub-6Â GHz, IEEE 802.11p and mmWave. IEEE Access 6, 1 (2017)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Zhang, H., Yong, X., Bu, S., Niyato, D., Yu, R., Zhu, H.: Computing in resource allocation three-tier IoT fog networks: a joint optimization approach combining stackelberg game and matching. IEEE Internet Things J. 4(5), 1204–1215 (2017)
Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. Computer 49, 112–116 (2016)
Ning, Z., Kong, X., Xia, F., Hou, W., Wang, X.: Green and sustainable cloud of things: enabling collaborative edge computing. IEEE Commun. Mag. 57, 72–78 (2019)
Greenberg, A.G., Hamilton, J.R., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev. 39, 68–73 (2008)
Goiri, I., Le, K., Guitart, J., Torres, J., Bianchini, R.: Intelligent placement of datacenters for internet services. In: 2011 31st International Conference on Distributed Computing Systems, pp. 131–142 (2011)
Yu, L., Chen, L., Cai, Z., Shen, H., Liang, Y., Pan, Y.: Stochastic load balancing for virtual resource management in datacenters. IEEE Trans. Cloud Comput. PP(99), 1 (2016)
Ahlgren, B., et al.: Content, connectivity, and cloud: ingredients for the network of the future. Commun. Mag. IEEE 49(7), 62–70 (2011)
Yannuzzi, M., Milito, R.A., Serral-Gracià , R., Montero, D., Nemirovsky, M.: Keyingredient sinan iotrecipe: fog computing, cloud computing, and more fog Computing. In: IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (2015)
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Sabella, D.: On multi-access edge computing: a survey of the emerging 5G network edge architecture and orchestration. IEEE Commun. Surv. Tutor. PP(99), 1 (2017)
Li, G., Xu, S., Wu, J., Ding, H.: Resource scheduling based on improved spectral clustering algorithm in edge computing. Sci. Program. 2018(5), 1–13 (2018)
Li, G., Liu, Y., Wu, J., Lin, D., Zhao, S.: Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors 19(2), 2122 (2019)
Kumar, N., Zeadally, S., Rodrigues, J.J.P.C.: Vehicular delay-tolerant networks for smart grid data management using mobile edge computing. IEEE Commun. Mag. 54(10), 60–66 (2016)
Hou, X., Yong, L., Min, C., Di, W., Jin, D., Sheng, C.: Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65(6), 3860–3873 (2016)
Zhang, W., Zhang, Z., Chao, H.C.: Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management. IEEE Commun. Mag. 55(12), 60–67 (2017)
He, L., Dong, M., Ota, K., Guo, M.: Pricing and repurchasing for big data processing in multi-clouds. IEEE Trans. Emerg. Top. Comput. 4(2), 1 (2016)
Cong, W., Ying, Y., Wang, C., Xi, H., Zheng, C.: Virtual bandwidth allocation game in data centers. In: IEEE International Conference on Information Science and Technology (2012)
Hao, W., Zhao, Y., Guan, H.: On pricing schemes in data center network with game theoretic approach. In: International Conference on Computer Communication and Networks (2014)
Pu-yan, N., Pei-ai, Z.: A note on Stackelberg games. In: 2008 Chinese Control and Decision Conference, pp. 1201–1203 (2008)
Jiang, Y., Chen, S.Z., Hu, B.: Stackelberg games-based distributed algorithm of pricing and resource allocation in heterogeneous wireless networks. J. Commun. 1, 61–68 (2013)
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Zhang, Y., Li, GS., Wu, JH., Yan, JH., Sheng, XF. (2020). Resource Allocation Algorithms of Vehicle Networks with Stackelberg Game. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_18
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DOI: https://doi.org/10.1007/978-3-030-48513-9_18
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