Alocação de Recursos em Redes Sem Fio de Grande Porte por meio de Meta-Heurísticas e Predição da Carga de Usuários
Resumo
Com o aumento significativo de usuários móveis, a busca pelo gerenciamento de recursos tornou-se essencial. Essa gerência deve visar atender à cobertura do sinal, mas, principalmente, manter o Acordo de Nível de Serviço desejado, independentemente do número de usuários conectados. Assim, propomos a utilização de quatro modelos de predição aplicado ao número de usuários conectados em uma rede sem fio. A partir dessas previsões, os recursos de rede podem ser alocados adequadamente. Investigamos o uso de Otimização de Enxame de Partículas e Algoritmo Genético para hiperparametrizar um Perceptron Multicamadas e uma Árvore de Decisão. Avaliamos nossa proposta utilizando dados reais de rede sem fio com mais de 20 mil usuários. Como resultado, obtivemos uma precisão média de 94,80%, melhorando consideravelmente a utilização de recursos da rede e atendendo um nível de acordo de serviço de 95%.
Referências
Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., and Tuba, M. (2020). Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms, 13(3):67.
Cai, W., Shea, R., Huang, C.-Y., Chen, K.-T., Liu, J., Leung, V. C., and Hsu, C.-H. (2016). A survey on cloud gaming: Future of computer games. IEEE Access, 4:7605-7620.
Carrascosa, M. and Bellalta, B. (2020). Cloud-gaming: Analysis of google stadia traffic. arXiv preprint arXiv:2009.09786.
Casas, P., Seufert, M., Wehner, N., Schwind, A., and Wamser, F. (2018). Enhancing machine learning based qoe prediction by ensemble models. In IEEE ICDCS.
Chih-Lin, I., Sun, Q., Liu, Z., Zhang, S., and Han, S. (2017). The big-data-driven intelligent wireless network: architecture, use cases, solutions, and future trends. IEEE vehicular technology magazine, 12(4):20-29.
Datsika, E., Kartsakli, E., Vardakas, J. S., Antonopoulos, A., Kalfas, G., Maniotis, P., Vagionas, C., Pleros, N., and Verikoukis, C. (2018). Qos-aware resource management for converged fiber wireless 5g fronthaul networks. In IEEE GLOBECOM, pages 1-5.
Deng, T., Wan, M., Shi, K., Zhu, L., Wang, X., and Jiang, X. (2021). Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network. SN Applied Sciences, 3(9).
Ferreira, Y. M., Frank, L. R., Julio, E. P., Ferreira, F. H. C., Dembogurski, B. J., and Silva, E. F. (2019). Applying a multilayer perceptron for traffic flow prediction to empower a smart ecosystem. In ICCSA, pages 633-648. Springer.
Ford, G., Cargan, R., Ahmed, A., Rigney, K., Berry, C., Bucci, D., and Kam, M. (2018). Wireless network traffic disaggregation using bayesian nonparametric techniques. In CISS, pages 1-6.
Frank, L. R., Ferreira, Y. M., Julio, E. P., Ferreira, F. H. C., Dembogurski, B. J., and Silva, E. F. (2019). Multilayer perceptron and particle swarm optimization applied to traffic flow prediction on smart cities. In ICCSA, pages 35-47. Springer.
Fu, Y., Wang, S., Wang, C.-X., Hong, X., and McLaughlin, S. (2018). Artificial intelligence to manage network traffic of 5g wireless networks. IEEE Network, 32(6):58-64.
Glantz, S. and Slinker, B. (2001). Primer of Applied Regression & Analysis of Variance, ed. McGraw-Hill, Inc., New York.
Goldberg, D. E. and Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning.
Ibrahim, A. A. Z. A., Kliazovich, D., and Bouvry, P. (2016). Service level agreement assurance between cloud services providers and cloud customers. In IEEE/ACM CCGrid.
Kafi, M. A., Mouradian, A., and Veque, V. (2019). Offline qos association scheme based on clients priorities and demands in wlan networks. In IEEE GLOBECOM, pages 1-6.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks, volume 4, pages 1942-1948. IEEE.
Lehmann, E. L. and Casella, G. (2006). Theory of point estimation. Springer Science & Business Media.
Li, M., Wang, Y., Wang, Z., and Zheng, H. (2020a). A deep learning method based on an attention mechanism for wireless network traffic prediction. Ad Hoc Networks, 107:102258.
Li, Y., Huang, J., and Chen, H. (2020b). Time series prediction of wireless network traffic flow based on wavelet analysis and BP neural network. Journal of Physics: Conference Series, 1533(3):032098.
Medeiros, D., Cunha Neto, H., Andreoni Lopez, M., Magalhaes, L., Silva, E., Vieira, A., Fernandes, N., and Mattos, D. (2019). Análise de dados em redes sem fio de grande porte: Processamento em fluxo em tempo real, tendências e desafios. MC-SBRC, 2019:142-195.
Prasad, P. S. and Agrawal, P. (2010). Movement prediction in wireless networks using mobility traces.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1):81-106.
Saikhu, A., Setyadi, A. T., Purwananto, Y., and Wijaya, A. Y. (2020). Spatio-temporal recurrent neural networks modeling for number of users prediction on wireless traffic networks. In ICICoS, pages 1-6.
Sone, S. P., Lehtomáˆki, J., Khan, Z., and Umebayashi, K. (2021). Forecasting wireless network traffic and channel utilization using real network/physical layer data. In EuCNC/6G Summit, pages 31-36.
Vieira, A. B., Paraizo, W. N., Chaves, L. J., Correia, L. H., and Silva, E. F. (2021). An sdnbased energy-aware traffic management mechanism. Annals of Telecommunications, pages 1-12.
Yu, Y., Liu, S., Wang, L., Teng, F., and Li, S. (2020). Traffic prediction model based on improved quantum particle swarm algorithm in wireless network. IOP Conference Series: Materials Science and Engineering, 768(6):062110.
Cai, W., Shea, R., Huang, C.-Y., Chen, K.-T., Liu, J., Leung, V. C., and Hsu, C.-H. (2016). A survey on cloud gaming: Future of computer games. IEEE Access, 4:7605-7620.
Carrascosa, M. and Bellalta, B. (2020). Cloud-gaming: Analysis of google stadia traffic. arXiv preprint arXiv:2009.09786.
Casas, P., Seufert, M., Wehner, N., Schwind, A., and Wamser, F. (2018). Enhancing machine learning based qoe prediction by ensemble models. In IEEE ICDCS.
Chih-Lin, I., Sun, Q., Liu, Z., Zhang, S., and Han, S. (2017). The big-data-driven intelligent wireless network: architecture, use cases, solutions, and future trends. IEEE vehicular technology magazine, 12(4):20-29.
Datsika, E., Kartsakli, E., Vardakas, J. S., Antonopoulos, A., Kalfas, G., Maniotis, P., Vagionas, C., Pleros, N., and Verikoukis, C. (2018). Qos-aware resource management for converged fiber wireless 5g fronthaul networks. In IEEE GLOBECOM, pages 1-5.
Deng, T., Wan, M., Shi, K., Zhu, L., Wang, X., and Jiang, X. (2021). Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network. SN Applied Sciences, 3(9).
Ferreira, Y. M., Frank, L. R., Julio, E. P., Ferreira, F. H. C., Dembogurski, B. J., and Silva, E. F. (2019). Applying a multilayer perceptron for traffic flow prediction to empower a smart ecosystem. In ICCSA, pages 633-648. Springer.
Ford, G., Cargan, R., Ahmed, A., Rigney, K., Berry, C., Bucci, D., and Kam, M. (2018). Wireless network traffic disaggregation using bayesian nonparametric techniques. In CISS, pages 1-6.
Frank, L. R., Ferreira, Y. M., Julio, E. P., Ferreira, F. H. C., Dembogurski, B. J., and Silva, E. F. (2019). Multilayer perceptron and particle swarm optimization applied to traffic flow prediction on smart cities. In ICCSA, pages 35-47. Springer.
Fu, Y., Wang, S., Wang, C.-X., Hong, X., and McLaughlin, S. (2018). Artificial intelligence to manage network traffic of 5g wireless networks. IEEE Network, 32(6):58-64.
Glantz, S. and Slinker, B. (2001). Primer of Applied Regression & Analysis of Variance, ed. McGraw-Hill, Inc., New York.
Goldberg, D. E. and Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning.
Ibrahim, A. A. Z. A., Kliazovich, D., and Bouvry, P. (2016). Service level agreement assurance between cloud services providers and cloud customers. In IEEE/ACM CCGrid.
Kafi, M. A., Mouradian, A., and Veque, V. (2019). Offline qos association scheme based on clients priorities and demands in wlan networks. In IEEE GLOBECOM, pages 1-6.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks, volume 4, pages 1942-1948. IEEE.
Lehmann, E. L. and Casella, G. (2006). Theory of point estimation. Springer Science & Business Media.
Li, M., Wang, Y., Wang, Z., and Zheng, H. (2020a). A deep learning method based on an attention mechanism for wireless network traffic prediction. Ad Hoc Networks, 107:102258.
Li, Y., Huang, J., and Chen, H. (2020b). Time series prediction of wireless network traffic flow based on wavelet analysis and BP neural network. Journal of Physics: Conference Series, 1533(3):032098.
Medeiros, D., Cunha Neto, H., Andreoni Lopez, M., Magalhaes, L., Silva, E., Vieira, A., Fernandes, N., and Mattos, D. (2019). Análise de dados em redes sem fio de grande porte: Processamento em fluxo em tempo real, tendências e desafios. MC-SBRC, 2019:142-195.
Prasad, P. S. and Agrawal, P. (2010). Movement prediction in wireless networks using mobility traces.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1):81-106.
Saikhu, A., Setyadi, A. T., Purwananto, Y., and Wijaya, A. Y. (2020). Spatio-temporal recurrent neural networks modeling for number of users prediction on wireless traffic networks. In ICICoS, pages 1-6.
Sone, S. P., Lehtomáˆki, J., Khan, Z., and Umebayashi, K. (2021). Forecasting wireless network traffic and channel utilization using real network/physical layer data. In EuCNC/6G Summit, pages 31-36.
Vieira, A. B., Paraizo, W. N., Chaves, L. J., Correia, L. H., and Silva, E. F. (2021). An sdnbased energy-aware traffic management mechanism. Annals of Telecommunications, pages 1-12.
Yu, Y., Liu, S., Wang, L., Teng, F., and Li, S. (2020). Traffic prediction model based on improved quantum particle swarm algorithm in wireless network. IOP Conference Series: Materials Science and Engineering, 768(6):062110.
Publicado
23/05/2022
Como Citar
FRANK, Lucas R.; CARNEVALE, Lorenzo; GALLETTA, Antonino; VILLARI, Massimo; VIEIRA, Alex B.; SILVA, Edelberto Franco.
Alocação de Recursos em Redes Sem Fio de Grande Porte por meio de Meta-Heurísticas e Predição da Carga de Usuários. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 6. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 29-42.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2022.223452.
