Alocação de Tarefas em Nuvens Veiculares Utilizando Jogos de Mercado
Resumo
Os dispositivos IoT representarão quase que metade de todos os dispositivos conectados mundialmente até 2023, sendo o setor de desenvolvimento de aplicações para carros conectados o de mais rápido crescimento. Com a incorporação cada vez maior de recursos computacionais aos veículos, o que viabiliza a criação de uma nova gama de aplicações, torna-se necessário alocar e gerenciar esses recursos de forma eficiente. Com isso em mente, este trabalho apresenta o ORANGE (alOcação de taRefAs em Nuvens utilizando joGos de mErcado), uma solução heurística para a alocação de tarefas em nuvens veiculares utilizando teoria dos jogos. A solução modela o sistema como um jogo de mercado que visa maximizar a utilização de recursos computacionais e considera diferentes taxas de requisição. O ORANGE foi avaliado com o uso extensivo de simulações e comparado a quatro outros algoritmos em 63 cenários. Considerando uma melhor utilização dos recursos, os resultados mostram que o ORANGE teve ganhos estatisticamente significativos de até 10.19% em relação ao segundo algoritmo melhor colocado. Confirmando assim, a eficácia da solução proposta.
Referências
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Olariu, S., Hristov, T., and Yan, G. (2013). The Next Paradigm Shift: From Vehicular Networks to Vehicular Clouds. In Basagni, S., Conti, M., Giordano, S., and Stojmenovic, I., editors, Mobile Ad Hoc Networking, pages 645–700. John Wiley & Sons, Inc., Hoboken, NJ, USA.
Paul, A., Chilamkurti, N., Daniel, A., and Rho, S. (2017). Vehicular network (VN) model. pages 43–75.
Peng, H. and Shen, X. (2020). Deep Reinforcement Learning Based Resource Management for Multi-Access Edge Computing in Vehicular Networks. IEEE Transactions on Network Science and Engineering, 7(4):2416–2428.
Pereira, R. S., Lieira, D. D., Silva, M. A. C. d., Pimenta, A. H. M., da Costa, J. B. D., Rosário, D., Villas, L., and Meneguette, R. I. (2020). Reliable: Resource allocation mechanism for 5g network using mobile edge computing. Sensors, 20(19).
Pereira, R. S. U., Lieira, D. D., da Silva, M. A. C., Pimenta, A. H. M., da Cost, J. B. D., Rosario, D., Meneguette, R. I., and Gutierrez, C. A. (2019). A novel fog-based resource allocation policy for vehicular clouds in the highway environment. 2019 Ieee Latinamerican Conference On Communications (ieee Latincom), page 6.
Shapley, L. S. (1965). On balanced sets and cores. Technical report, RAND CORP SANTA MONICA CALIF.
Sun, Z., Liu, Y., Wang, J., Li, G., Anil, C., Li, K., Guo, X., Sun, G., Tian, D., and Cao, D. (2021). Applications of Game Theory in Vehicular Networks: A Survey. 23(4):2660–2710.
Wei, W., Yang, R., Gu, H., Zhao, W., Chen, C., and Wan, S. (2021). Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks. IEEE Transactions on Intelligent Transportation Systems, pages 1–10. Conference Name: IEEE Transactions on Intelligent Transportation Systems.
Yang, W., Zhang, R., Chen, C., and Cheng, X. (2017). Secrecy-Based Resource Allocation for Vehicular Communication Networks with Outdated CSI. In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pages 1–5.
Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., and Zhang, Y. (2015). Cooperative Resource Management in Cloud-Enabled Vehicular Networks. IEEE Transactions on Industrial Electronics, 62(12):7938–7951.
Bondareva, O. N. (1963). Some applications of linear programming methods to the theory of cooperative games. Problemy kibernetiki, 10(119):139.
CISCO (2020). Cisco Annual Internet Report - Cisco Annual Internet Report (2018–2023) White Paper. [link]. Acessado em 4 de Novembro, 2021.
Codeca, L., Frank, R., Faye, S., and Engel, T. (2017). Luxembourg SUMO Traffic (LuST) Scenario: Traffic Demand Evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2):52–63.
Cordeschi, N., Amendola, D., Shojafar, M., and Baccarelli, E. (2015). Distributed and adaptive resource management in Cloud-assisted Cognitive Radio Vehicular Networks with hard reliability guarantees. Vehicular Communications, 2(1):1–12.
Coutinho, R. W. L. and Boukerche, A. (2019). Guidelines for the Design of Vehicular Cloud Infrastructures for Connected Autonomous Vehicles. IEEE Wireless Communications, 26(4):6–11.
da Costa, J. B. D., Peixoto, M. L. M., Meneguette, R. I., Rosário, D. L., and Villas, L. A. (2020). MORFEU: Mecanismo baseado em Otimização Combinatória para Alocação de Tarefas em Nuvens Veiculares. In Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 505–518. SBC.
Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(1):1–30.
Elahi, M. M., Rahman, M. M., and Islam, M. M. (2022). An efficient authentication scheme for secured service provisioning in edge-enabled vehicular cloud networks towards sustainable smart cities. Sustainable Cities and Society, 76:103384.
Eltoweissy, M., Olariu, S., and Younis, M. (2010). Towards Autonomous Vehicular Clouds. In Zheng, J., Simplot-Ryl, D., and Leung, V. C. M., editors, Ad Hoc Networks, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pages 1–16, Berlin, Heidelberg. Springer.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, pages 226–231, Portland, Oregon. AAAI Press.
Hattab, G., Ucar, S., Higuchi, T., Altintas, O., Dressler, F., and Cabric, D. (2019). Optimized Assignment of Computational Tasks in Vehicular Micro Clouds. In Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking, EdgeSys ’19, pages 1–6, New York, NY, USA. Association for Computing Machinery.
Kadhim, A. J. and Hosseini Seno, S. A. (2019). Maximizing the Utilization of Fog Computing in Internet of Vehicle Using SDN. IEEE Communications Letters, 23(1):140–143.
Lieira, D. D., Quessada, M. S., Cristiani, A. L., and Meneguette, R. I. (2020). Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm. In 2020 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.
Liu, L., Chen, C., Pei, Q., Maharjan, S., and Zhang, Y. (2021). Vehicular Edge Computing and Networking: A Survey. Mobile Networks and Applications, 26(3):1145–1168.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic traffic simulation using sumo. In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE.
Marques, H. A. P. and Meneguette, R. I. (2021). Um Mecanismo de Alocação de Recursos em Nuvens Veiculares baseado em Teoria dos Jogos. In Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 241–248. SBC.
Maschler, M., Solan, E., and Zamir, S. (2020). Game Theory.
Meneguette, R. I., Boukerche, A., and Pimenta, A. H. M. (2019a). Avarac: An availability-based resource allocation scheme for vehicular cloud. IEEE Transactions on Intelligent Transportation Systems, 20(10):3688–3699.
Meneguette, R. I., Rodrigues, D. O., da Costa, J. B. D., Rosario, D., and Villas, L. A. (2019b). A Virtual Machine Migration Policy Based on Multiple Attribute Decision in Vehicular Cloud Scenario. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pages 1–6.
Nabi, M., Benkoczi, R., Abdelhamid, S., and Hassanein, H. S. (2017). Resource assignment in vehicular clouds. In 2017 IEEE International Conference on Communications (ICC), pages 1–6. Ieee.
Olariu, S., Hristov, T., and Yan, G. (2013). The Next Paradigm Shift: From Vehicular Networks to Vehicular Clouds. In Basagni, S., Conti, M., Giordano, S., and Stojmenovic, I., editors, Mobile Ad Hoc Networking, pages 645–700. John Wiley & Sons, Inc., Hoboken, NJ, USA.
Paul, A., Chilamkurti, N., Daniel, A., and Rho, S. (2017). Vehicular network (VN) model. pages 43–75.
Peng, H. and Shen, X. (2020). Deep Reinforcement Learning Based Resource Management for Multi-Access Edge Computing in Vehicular Networks. IEEE Transactions on Network Science and Engineering, 7(4):2416–2428.
Pereira, R. S., Lieira, D. D., Silva, M. A. C. d., Pimenta, A. H. M., da Costa, J. B. D., Rosário, D., Villas, L., and Meneguette, R. I. (2020). Reliable: Resource allocation mechanism for 5g network using mobile edge computing. Sensors, 20(19).
Pereira, R. S. U., Lieira, D. D., da Silva, M. A. C., Pimenta, A. H. M., da Cost, J. B. D., Rosario, D., Meneguette, R. I., and Gutierrez, C. A. (2019). A novel fog-based resource allocation policy for vehicular clouds in the highway environment. 2019 Ieee Latinamerican Conference On Communications (ieee Latincom), page 6.
Shapley, L. S. (1965). On balanced sets and cores. Technical report, RAND CORP SANTA MONICA CALIF.
Sun, Z., Liu, Y., Wang, J., Li, G., Anil, C., Li, K., Guo, X., Sun, G., Tian, D., and Cao, D. (2021). Applications of Game Theory in Vehicular Networks: A Survey. 23(4):2660–2710.
Wei, W., Yang, R., Gu, H., Zhao, W., Chen, C., and Wan, S. (2021). Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks. IEEE Transactions on Intelligent Transportation Systems, pages 1–10. Conference Name: IEEE Transactions on Intelligent Transportation Systems.
Yang, W., Zhang, R., Chen, C., and Cheng, X. (2017). Secrecy-Based Resource Allocation for Vehicular Communication Networks with Outdated CSI. In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pages 1–5.
Yu, R., Huang, X., Kang, J., Ding, J., Maharjan, S., Gjessing, S., and Zhang, Y. (2015). Cooperative Resource Management in Cloud-Enabled Vehicular Networks. IEEE Transactions on Industrial Electronics, 62(12):7938–7951.
Publicado
23/05/2022
Como Citar
R. JÚNIOR, Aguimar; COSTA, Joahannes B. D. da; ROCHA FILHO, Geraldo P.; VILLAS, Leandro A.; GUIDONI, Daniel L.; MENEGUETTE, Rodolfo I..
Alocação de Tarefas em Nuvens Veiculares Utilizando Jogos de Mercado. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 210-223.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2022.222247.
