ARCANE: Algoritmo Meta-heurístico para Alocação de Tarefas em Nuvens Veiculares
Resumo
O avanço dos Sistemas de Transporte Inteligentes (ITS) vem para auxiliar a resolver problemas de tráfego, que atualmente geram problemas socioeconômicos. Entretanto, devido a dinamicidade da rede em que os ITS atuam, a alta mobilidade dos veículos e a constante mudança de topologia faz com que o problema de alocação de recursos e tarefas se tornem ainda mais desafiador. Diante desse desafio, é proposto o ARCANE, um algoritmo meta-heurístico para alocação de tarefas em nuvens veiculares. O ARCANE é um método bio-inspirado baseado no Algoritmo do Morcego (BAT). O objetivo do ARCANE é otimizar o processo de busca para fornecer soluções subótimas no processo de alocação de recursos e tarefas em uma nuvem veicular. Quando comparado com outas soluções da literatura, o ARCANE mostrou ser efetivo em alocar tarefas, aproveitando melhor os recursos das nuvens veiculares em todos os cenários.
Referências
Assis, C. (2021). Billions poured into electric-vehicle companies, but much more will be needed before the auto industry changes. Acessado em: 16 de abril de 2021.
Codeca, L., Frank, R., and Engel, T. (2015). Luxembourg sumo traffic (lust) scenario: 24 hours of mobility for vehicular networking research. In VNC, pages 1-8.
Correa, C., Ueyama, J., Meneguette, R. I., and Villas, L. A. (2014). Vanets: An exploratory evaluation in vehicular ad hoc network for urban environment. In 2014 IEEE 13th International Symposium on Network Computing and Applications, pages 45-49.
da Costa, J. B. D., Meneguette, R. I., Rosário, D., and Villas, L. A. (2020). Combinatorial optimization-based task allocation mechanism for vehicular clouds. In VTC2020Spring. IEEE.
Demir, M. S., Sait, S. M., and Uysal, M. (2018). Unified resource allocation and mobility management technique using particle swarm optimization for vlc networks. IEEE Photonics Journal, 10(6):1-9.
Faris, H., Aljarah, I., Mirjalili, S., Castillo, P. A., and Merelo, J. J. (2016). EvoloPy: An open-source nature-inspired optimization framework in python. In Proceedings of the 8th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications.
Feng, M., Guomin, L., and Wenrong, G. (2018). Heterogeneous network resource allocation optimization based on improved bat algorithm. In 2018 International Conference on Sensor Networks and Signal Processing (SNSP), pages 55-59.
Jayswal, A. K. (2020). Efficient task allocation for cloud using bat algorithm. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), pages 186-190.
Katoch, S., Chauhan, S. S., and Kumar, V. (2020). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80(5):8091-8126.
Liang, J. J., Suganthan, P. N., and Deb, K. (2005). Novel composition test functions for numerical global optimization. In Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., pages 68-75.
Liao, C., Wu, J., Du, J., and Zhao, L. (2017). Ant colony optimization inspired resource allocation for multiuser multicarrier systems. In 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), pages 1-6.
Lieira, D. D., Quessada, M. S., Cristiani, A. L., and Meneguette, R. I. (2021). Algorithm for 5g resource management optimization in edge computing. ieee latin america transactions. IEEE Latin America Transactions, 19(10).
Meneguette, R., De Grande, R., Ueyama, J., Filho, G. P. R., and Madeira, E. (2021). Vehicular edge computing: Architecture, resource management, security, and challenges. 55(1).
Meneguette, R. I. and Boukerche, A. (2017). A cooperative and adaptive resource scheduling for vehicular cloud. In 2017 IEEE Symposium on Computers and Communications (ISCC), pages 398-403.
Midya, S., Roy, A., Majumder, K., and Phadikar, S. (2018). Pso based optimized resource allocation in three tier cloud architecture for vanet. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pages 12-17.
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.
Okwu, M. O. and Tartibu, L. K. (2021). Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing.
Pereira, R. S., Lieira, D. D., da Silva, M. A. C., Pimenta, A. H. M., da Costa, J. B. D., Rosário, D., and Meneguette, R. I. (2019). A novel fog-based resource allocation policy for vehicular clouds in the highway environment. In 2019 IEEE Latin-American Conference on Communications (LATINCOM), pages 1-6.
Pereira, R. S., Lieira, D. D., Silva, M. A. C. d., Pimenta, A. H. M., da Costa, J. B. D., Rosario, D., Villas, L., and Meneguette, R. I. (2020). Reliable: Resource allocation mechanism for 5g network using mobile edge computing. Sensors, 20(19).
Rocha Filho, G. P., Meneguette, R. I., Torres Neto, J. R., Valejo, A., Weigang, L., Ueyama, J., Pessin, G., and Villas, L. A. (2020). Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities. Ad Hoc Networks, 107:102265.
Srivastava, S. and Sahana, S. K. (2019). Application of bat algorithm for transport network design problem. Applied Computational Intelligence and Soft Computing, 2019:9864090.
Vikhar, P. A. (2016). Evolutionary algorithms: A critical review and its future prospects. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pages 261-265.
Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pages 65-74. Springer Berlin Heidelberg.
Zhang, T., Grande, R. E. D., and Boukerche, A. (2015). Vehicular cloud. In Proceedings of the 12th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks PE-WASUN '15. ACM Press.
Codeca, L., Frank, R., and Engel, T. (2015). Luxembourg sumo traffic (lust) scenario: 24 hours of mobility for vehicular networking research. In VNC, pages 1-8.
Correa, C., Ueyama, J., Meneguette, R. I., and Villas, L. A. (2014). Vanets: An exploratory evaluation in vehicular ad hoc network for urban environment. In 2014 IEEE 13th International Symposium on Network Computing and Applications, pages 45-49.
da Costa, J. B. D., Meneguette, R. I., Rosário, D., and Villas, L. A. (2020). Combinatorial optimization-based task allocation mechanism for vehicular clouds. In VTC2020Spring. IEEE.
Demir, M. S., Sait, S. M., and Uysal, M. (2018). Unified resource allocation and mobility management technique using particle swarm optimization for vlc networks. IEEE Photonics Journal, 10(6):1-9.
Faris, H., Aljarah, I., Mirjalili, S., Castillo, P. A., and Merelo, J. J. (2016). EvoloPy: An open-source nature-inspired optimization framework in python. In Proceedings of the 8th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications.
Feng, M., Guomin, L., and Wenrong, G. (2018). Heterogeneous network resource allocation optimization based on improved bat algorithm. In 2018 International Conference on Sensor Networks and Signal Processing (SNSP), pages 55-59.
Jayswal, A. K. (2020). Efficient task allocation for cloud using bat algorithm. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), pages 186-190.
Katoch, S., Chauhan, S. S., and Kumar, V. (2020). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80(5):8091-8126.
Liang, J. J., Suganthan, P. N., and Deb, K. (2005). Novel composition test functions for numerical global optimization. In Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., pages 68-75.
Liao, C., Wu, J., Du, J., and Zhao, L. (2017). Ant colony optimization inspired resource allocation for multiuser multicarrier systems. In 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), pages 1-6.
Lieira, D. D., Quessada, M. S., Cristiani, A. L., and Meneguette, R. I. (2021). Algorithm for 5g resource management optimization in edge computing. ieee latin america transactions. IEEE Latin America Transactions, 19(10).
Meneguette, R., De Grande, R., Ueyama, J., Filho, G. P. R., and Madeira, E. (2021). Vehicular edge computing: Architecture, resource management, security, and challenges. 55(1).
Meneguette, R. I. and Boukerche, A. (2017). A cooperative and adaptive resource scheduling for vehicular cloud. In 2017 IEEE Symposium on Computers and Communications (ISCC), pages 398-403.
Midya, S., Roy, A., Majumder, K., and Phadikar, S. (2018). Pso based optimized resource allocation in three tier cloud architecture for vanet. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pages 12-17.
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.
Okwu, M. O. and Tartibu, L. K. (2021). Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing.
Pereira, R. S., Lieira, D. D., da Silva, M. A. C., Pimenta, A. H. M., da Costa, J. B. D., Rosário, D., and Meneguette, R. I. (2019). A novel fog-based resource allocation policy for vehicular clouds in the highway environment. In 2019 IEEE Latin-American Conference on Communications (LATINCOM), pages 1-6.
Pereira, R. S., Lieira, D. D., Silva, M. A. C. d., Pimenta, A. H. M., da Costa, J. B. D., Rosario, D., Villas, L., and Meneguette, R. I. (2020). Reliable: Resource allocation mechanism for 5g network using mobile edge computing. Sensors, 20(19).
Rocha Filho, G. P., Meneguette, R. I., Torres Neto, J. R., Valejo, A., Weigang, L., Ueyama, J., Pessin, G., and Villas, L. A. (2020). Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities. Ad Hoc Networks, 107:102265.
Srivastava, S. and Sahana, S. K. (2019). Application of bat algorithm for transport network design problem. Applied Computational Intelligence and Soft Computing, 2019:9864090.
Vikhar, P. A. (2016). Evolutionary algorithms: A critical review and its future prospects. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pages 261-265.
Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pages 65-74. Springer Berlin Heidelberg.
Zhang, T., Grande, R. E. D., and Boukerche, A. (2015). Vehicular cloud. In Proceedings of the 12th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks PE-WASUN '15. ACM Press.
Publicado
23/05/2022
Como Citar
QUESSADA, Matheus S.; LIEIRA, Douglas D.; COSTA, Joahannes B. D. da; ROCHA FILHO, Geraldo P.; GRANDE, Robson E. De; MENEGUETTE, Rodolfo I..
ARCANE: Algoritmo Meta-heurístico para Alocação de Tarefas em Nuvens Veiculares. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 6. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 140-153.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2022.223498.
