Estimativa de Qualidade de Serviço em Estações Wi-Fi Utilizando Redes Neurais Recorrentes
Resumo
As redes sem fio são a maneira mais comum de acessar a Internet, com mais de 10 bilhões de dispositivos Wi-Fi já vendidos. As conexões sem fio sofrem problemas relacionados ao uso excessivo do espectro, a erros de transmissão e a perda de informações. Sistemas inteligentes de controle são uma alternativa para o gerenciamento eficiente da rede, visando à melhoria da Qualidade de Serviço (QoS). No entanto, o primeiro passo é obter uma função que relaciona as configurações de Wi-Fi atuais a um valor de QoS esperado. Este trabalho propõe um modelo que utiliza RNN para inferir tal relação, baseado em dados reais de Wi-Fi. Ele compara duas RNNs: GRU e LSTM. O modelo prevê quatro métricas de rede: vazão, perda, atraso e variação do atraso (jitter), com base apenas nos dados de tráfego obtidos no AP. O RMSE médio é da ordem de 10-2 para vazão, de 10-4 para o atraso, e de 10-5 para a jitter e para a perda de pacotes para ambos os métodos.
Palavras-chave:
Redes Sem Fio, Qualidade de Serviço, Gerenciamento de Redes, Redes Neurais
Referências
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Biswas, S., Bicket, J., Wong, E., Musaloiu-e, R., Bhartia, A., and Aguayo, D. (2015). Large-scale measurements of wireless network behavior. In ACM SIGCOMM CCR, pages 153–165.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Empirical Methods in Natural Language Processing.
Crawley, E. S., Nair, R., Rajagopalan, B., and Sandick, H. (1998). A Framework for QoS-based Routing in the Internet. RFC 2386, RFC Editor. http: //www.rfc-editor.org/rfc/rfc2386.txt.
DiCioccio, L., Teixeira, R., and Rosenberg, C. (2013). Measuring Home Networks with Homenet Profiler . In 14th PAM, pages 176–186.
Dixit, A., Hao, F., Mukherjee, S., Lakshman, T., and Kompella, R. (2013). Towards an elastic distributed SDN controller. In ACM SIGCOMM CCR, volume 43. ACM.
Ehsan, S. and Hamdaoui, B. (2012). A survey on energyefficient routing techniques with QoS assurances for wireless multimedia sensor networks. IEEE COMST, 14(2):265–278.
Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2):179– 211.
Fazio, P., Tropea, M., Marano, S., and Sottile, C. (2013). Pattern prediction in infrastructured wireless networks: Directional vs temporal statistical approach. In Wireless Days, IEEE/IFIP IM, pages 1–5. IEEE.
Gelenbe, E., Lent, R., Montuori, A., and Xu, Z. (2002). Cognitive packet networks: QoS and performance. In 10th MASCOTS, pages 3–9. IEEE.
Gers, F. A., Schmidhuber, J., and Cummins, F. (1999). Learning to forget: Continual prediction with LSTM. In 9th ICANN. IET.
Gomes, R. L., Júnior, J. J., Abelém, A. G., and Júnior, W. M. (2009). QoE and QoS support onWireless Mesh Networks. In Proceedings of the XV Brazilian Symposium on Multimedia and the Web, page 2. ACM.
Gozdecki, J., Jajszczyk, A., and Stankiewicz, R. (2003). Quality of service terminology in IP networks. IEEE Communications Magazine, 41(3):153–159.
Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory. Neural computation, 9(8):1735–1780.
Jozefowicz, R., Zaremba, W., and Sutskever, I. (2015). An Empirical Exploration of Recurrent Network Architectures. In Proceedings of the 32nd ICML, pages 2342–2350.
Moura, H., Bessa, G. V. C., Vieira, M. A. M., and Macedo, D. F. (2015). Ethanol: Software Defined Networking for 802.11 Wireless Networks . In 14th IEEE/IFIP IM, pages 388–396.
Moura, H. D., Macedo, D. F., and Vieira, M. A. M. (2019). Automatic Quality of Experience Management for WLAN Networks using Multi-Armed Bandit. IEEE/IFIP IM, pages 1–8.
Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A. I., and Dai, H. (2018). A survey on low latency towards 5G: RAN, core network and caching solutions. IEEE COMST.
Paudel, I., Pokhrel, J., Wehbi, B., Cavalli, A., and Jouaber, B. (2014). Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach. In 2014 14th ISCIT, pages 51–55.
Pedras, V., Sousa, M., Vieira, P., Queluz, M. P., and Rodrigues, A. (2018). A no-reference user centric QoE model for voice and web browsing based on 3G/4G radio measurements. In 2018 IEEE WCNC, pages 1–6. IEEE.
Pokhrel, J.,Wehbi, B., Morais, A., Cavalli, A., and Allilaire, E. (2013). Estimation of QoE of video traffic using a fuzzy expert system. In 2013 IEEE 10th CCNC, pages 224–229.
Skansi, S. (2018). Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence. Springer.
Tootoonchian, A., Gorbunov, S., Ganjali, Y., Casado, M., and Sherwood, R. (2012). On Controller Performance in Software-Defined Networks. Hot- ICE, 12:1–6.
White, G., Palade, A., and Clarke, S. (2018). Forecasting QoS Attributes Using LSTM Networks. In 2018 IJCNN.
Publicado
06/05/2019
Como Citar
MIRANDA JR., Gilson; MOURA, Henrique D.; NUNES, Matheus H. N.; CORREIA, Luiz H. A.; MACEDO, Daniel F..
Estimativa de Qualidade de Serviço em Estações Wi-Fi Utilizando Redes Neurais Recorrentes. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 37. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 57-70.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2019.7350.
