Análise não supervisionada para inferência de qualidade de experiência de usuários residenciais
Resumo
A avaliação da qualidade de experiência dos usuários residenciais é de grande interesse para ISPs. No entanto, a obtenção da QoE percebida é custosa, dificultando a utilização de classificadores supervisionados. Este trabalho propõe um método baseado em aprendizado de máquina não-supervisionado que deteta padrões estatísticos nas séries temporais a partir da deteção de pontos de mudança e da correlação espaço-temporal dos resultados de medições de QoS. Exemplificamos a aplicação do método em um conjunto de dados reais, mostrando que os resultados do modelo refletem uma métrica de QoE dos usuários obtida a partir de chamados técnicos realizados para o call center. Por fim, avaliamos a acurácia da execução online do método.
Palavras-chave:
Provedores de Serviço de Internet, qualidade de experiência, Aprendizado de Máquina
Referências
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Bustamante, F., Clark, D., and Feamster, N. (2017). Workshop on Tracking Quality of Experience in the Internet: Summary and Outcomes. SIGCOMM Comput. Commun. Rev., 47(1):55–60.
Charonyktakis, P., Plakia, M., Tsamardinos, I., and Papadopouli, M. (2016). On usercentric modular QoE prediction for VoIP based on machine-learning algorithms. IEEE Transactions on Mobile Computing, 15:1443–1456.
da Silva, A. P. C., Varela, M., de Souza e Silva, E., Leão, R. M. M., and Rubino, G. (2008). Quality assessment of interactive voice applications. Computer Networks, 52(6):1179–1192.
de Souza e Silva, E., Leão, R. M. M., and Muntz., R. R. (2011). Performance evaluation with hidden markov models. In Performance Evaluation of Computer and Communication Systems. Milestones and Future Challenges, pages 112–128.
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Hoßfeld, T., Keimel, C., Hirth, M., Gardlo, B., Habigt, J., Diepold, K., and Tran-Gia, P. (2014). Best practices for QoE crowdtesting: QoE assessment with crowdsourcing. IEEE Transactions on Multimedia, 16(2):541–558.
Hora, D. N. d., Teixeira, R., van Doorselaer, K., and van Oost, K. (2016). Predicting the effect of home Wi-Fi quality on web QoE. In Proceedings of the 2016 Workshop on QoE-based Analysis and Management of Data Communication Networks, Internet- QoE ’16, pages 13–18.
Kehagias, A. (2004). A hidden markov model segmentation procedure for hydrological and environmental time series. Stochastic Environmental Research and Risk Assessment, 18(2):117–130.
Luong, T. M., Rozenholc, Y., and Nuel, G. (2013). Fast estimation of posterior probabilities in change-point analysis through a constrained hidden markov model. Computational Statistics and Data Analysis, 68:129 – 140.
Maidstone, R., Hocking, T., Rigaill, G., and Fearnhead, P. (2017). On optimal multiple changepoint algorithms for large data. Statistics and Computing, 27(2):519–533.
Monta˜nez, G. D., Amizadeh, S., and Laptev, N. (2015). Inertial hidden markov models: Modeling change in multivariate time series. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI’15, pages 1819–1825. AAAI Press.
Padmanabha Iyer, A., Erran Li, L., Chowdhury, M., and Stoica, I. (2018). Mitigating the latency-accuracy trade-off in mobile data analytics systems. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, pages 513–528. ACM.
Pan, H., Zhou, S., Jia, Y., Niu, Z., Zheng, M., and Geng, L. (2018). Data-driven user complaint prediction for mobile access networks. Journal of Communications and Information Networks, 3(3):9–19.
Parhami, B. (1994). Voting algorithms. IEEE transactions on reliability, 43(4):617–629.
Peng, Y., Yang, J.,Wu, C., Guo, C., Hu, C., and Li, Z. (2017). deTector: a Topology-aware Monitoring System for Data Center Networks. In 2017 USENIX Annual Technical Conference (USENIX ATC 17), pages 55–68.
Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE.
Sundaresan, S., de Donato, W., N. Feamster, Teixeira, R., Crawford, S., and Pescapè, A. (2011). Broadband internet performance: A view from the gateway. In Proceedings of the ACM SIGCOMM 2011.
Ximenes, D., Mendonça, G., Santos, G. H. A., de Souza e Silva, E., Leão, R. M., and Menasché, D. S. (2018). O problema de detecção e localização de eventos em séries temporais aplicado a redes de computadores.
Workshop em Desempenho de Sistemas Computacionais e de Comunicação (WPerformance CSBC), 17(1/2018).
Yan, H., Flavel, A., Ge, Z., Gerber, A., Massey, D., Papadopoulos, C., Shah, H., and Yates, J. (2012). Argus: End-to-end service anomaly detection and localization from an isp’s point of view. In INFOCOM, 2012 Proceedings IEEE, pages 2756–2760.
Yu, S.-Z. and Kobayashi, H. (2003). A hidden semi-markov model with missing data and multiple observation sequences for mobility tracking. Signal Processing, 83(2):235–250.
Aminikhanghahi, S. and Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and information systems, 51(2):339–367.
Bustamante, F., Clark, D., and Feamster, N. (2017). Workshop on Tracking Quality of Experience in the Internet: Summary and Outcomes. SIGCOMM Comput. Commun. Rev., 47(1):55–60.
Charonyktakis, P., Plakia, M., Tsamardinos, I., and Papadopouli, M. (2016). On usercentric modular QoE prediction for VoIP based on machine-learning algorithms. IEEE Transactions on Mobile Computing, 15:1443–1456.
da Silva, A. P. C., Varela, M., de Souza e Silva, E., Leão, R. M. M., and Rubino, G. (2008). Quality assessment of interactive voice applications. Computer Networks, 52(6):1179–1192.
de Souza e Silva, E., Leão, R. M. M., and Muntz., R. R. (2011). Performance evaluation with hidden markov models. In Performance Evaluation of Computer and Communication Systems. Milestones and Future Challenges, pages 112–128.
Deb, S., Ge, Z., Isukapalli, S., Puthenpura, S., Venkataraman, S., Yan, H., and Yates, J. (2017). Aesop: Automatic policy learning for predicting and mitigating network service impairments. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1783–1792.
Hoßfeld, T., Keimel, C., Hirth, M., Gardlo, B., Habigt, J., Diepold, K., and Tran-Gia, P. (2014). Best practices for QoE crowdtesting: QoE assessment with crowdsourcing. IEEE Transactions on Multimedia, 16(2):541–558.
Hora, D. N. d., Teixeira, R., van Doorselaer, K., and van Oost, K. (2016). Predicting the effect of home Wi-Fi quality on web QoE. In Proceedings of the 2016 Workshop on QoE-based Analysis and Management of Data Communication Networks, Internet- QoE ’16, pages 13–18.
Kehagias, A. (2004). A hidden markov model segmentation procedure for hydrological and environmental time series. Stochastic Environmental Research and Risk Assessment, 18(2):117–130.
Luong, T. M., Rozenholc, Y., and Nuel, G. (2013). Fast estimation of posterior probabilities in change-point analysis through a constrained hidden markov model. Computational Statistics and Data Analysis, 68:129 – 140.
Maidstone, R., Hocking, T., Rigaill, G., and Fearnhead, P. (2017). On optimal multiple changepoint algorithms for large data. Statistics and Computing, 27(2):519–533.
Monta˜nez, G. D., Amizadeh, S., and Laptev, N. (2015). Inertial hidden markov models: Modeling change in multivariate time series. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI’15, pages 1819–1825. AAAI Press.
Padmanabha Iyer, A., Erran Li, L., Chowdhury, M., and Stoica, I. (2018). Mitigating the latency-accuracy trade-off in mobile data analytics systems. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, pages 513–528. ACM.
Pan, H., Zhou, S., Jia, Y., Niu, Z., Zheng, M., and Geng, L. (2018). Data-driven user complaint prediction for mobile access networks. Journal of Communications and Information Networks, 3(3):9–19.
Parhami, B. (1994). Voting algorithms. IEEE transactions on reliability, 43(4):617–629.
Peng, Y., Yang, J.,Wu, C., Guo, C., Hu, C., and Li, Z. (2017). deTector: a Topology-aware Monitoring System for Data Center Networks. In 2017 USENIX Annual Technical Conference (USENIX ATC 17), pages 55–68.
Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE.
Sundaresan, S., de Donato, W., N. Feamster, Teixeira, R., Crawford, S., and Pescapè, A. (2011). Broadband internet performance: A view from the gateway. In Proceedings of the ACM SIGCOMM 2011.
Ximenes, D., Mendonça, G., Santos, G. H. A., de Souza e Silva, E., Leão, R. M., and Menasché, D. S. (2018). O problema de detecção e localização de eventos em séries temporais aplicado a redes de computadores.
Workshop em Desempenho de Sistemas Computacionais e de Comunicação (WPerformance CSBC), 17(1/2018).
Yan, H., Flavel, A., Ge, Z., Gerber, A., Massey, D., Papadopoulos, C., Shah, H., and Yates, J. (2012). Argus: End-to-end service anomaly detection and localization from an isp’s point of view. In INFOCOM, 2012 Proceedings IEEE, pages 2756–2760.
Yu, S.-Z. and Kobayashi, H. (2003). A hidden semi-markov model with missing data and multiple observation sequences for mobility tracking. Signal Processing, 83(2):235–250.
Publicado
06/05/2019
Como Citar
SANTOS, Gustavo H. A.; MENDONÇA, Gabriel; DE SOUZA E SILVA, Edmundo; LEÃO, Rosa M. M.; MENASCHÉ, Daniel S..
Análise não supervisionada para inferência de qualidade de experiência de usuários residenciais. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 37. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 958-971.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2019.7415.
