Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns

[EN] Nowadays, there is high interest in modeling the type of multimedia traffic with the purpose of estimating the network resources required to guarantee the quality delivered to the user. In this work we propose a multimedia traffic classification model based on patterns that allows us to differentiate the type of traffic by using video streaming and network characteristics as input parameters. We show that there is low correlation between network parameters and the delivered video quality. Because of this, in addition to network parameters, we also add video streaming parameters in order to improve the efficiency of our system. Finally, it should be noted that, based on the objective video quality received by the user, we have extracted traffic patterns that we use to perform the development of the classification model.This work has been supported by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the Project with reference TIN2017-84802-C2-1-P.Canovas Solbes, A.; Jimenez, JM.; Romero Martínez, JO.; Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network. 32(6):100-107. doi:10.1109/MNET.2018.180012110010732

A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

[EN] Nowadays, network infrastructures such as Software Defined Networks (SDN) achieve a huge computational power. This allows to add a high processing on the network nodes. In this paper, a multimedia traffic management system is presented. This system is based on estimation models of Quality of Experience (QoE) and also on the traffic patterns classification. In order to achieve this, a QoE estimation method has been modeled. This method allows for classifying the multimedia traffic from multimedia transmission patterns. In order to do this, the SDN controller gathers statistics from the network. The patterns used have been defined from a lineal combination of objective QoE measurements. The model has been defined by Bayesian regularized neural networks (BRNN). From this model, the system is able to classify several kind of traffic according to the quality perceived by the users. Then, a model has been developed to determine which video characteristics need to be changed to provide the user with the best possible quality in the critical moments of the transmission. The choice of these characteristics is based on the quality of service (QoS) parameters, such as delay, jitter, loss rate and bandwidth. Moreover, it is also based on subpatterns defined by clusters from the dataset and which represents network and video characteristics. When a critical network situation is given, the model selects, by using network parameters as entries, the subpattern with the most similar network condition. The minimum Euclidean distance between these entries and the network parameters of the subpatters is calculated to perform this selection. Both models work together to build a reliable multimedia traffic management system perfectly integrated into current network infrastructures, which is able to classify the traffic and solve critical situations changing the video characteristics, by using the SDN architecture.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formation del Profesorado Universitario FPU (Convocatoria 2015)", grant number FPU15/06837 and by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigation Cientffica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Canovas Solbes, A.; Rego Mañez, A.; Romero Martínez, JO.; Lloret, J. (2020). A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN. Journal of Network and Computer Applications. 150:1-14. https://doi.org/10.1016/j.jnca.2019.102498S114150Cánovas, A., Taha, M., Lloret, J., & Tomás, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications, 104, 107-116. doi:10.1016/j.jnca.2017.12.020Canovas, A., Jimenez, J. M., Romero, O., & Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network, 32(6), 100-107. doi:10.1109/mnet.2018.1800121Burden, F., & Winkler, D. (2008). Bayesian Regularization of Neural Networks. Artificial Neural Networks, 23-42. doi:10.1007/978-1-60327-101-1_3Goodman, S. N. (2005). Introduction to Bayesian methods I: measuring the strength of evidence. Clinical Trials, 2(4), 282-290. doi:10.1191/1740774505cn098oaHirschen, K., & Schäfer, M. (2006). Bayesian regularization neural networks for optimizing fluid flow processes. Computer Methods in Applied Mechanics and Engineering, 195(7-8), 481-500. doi:10.1016/j.cma.2005.01.015Huang, X., Yuan, T., Qiao, G., & Ren, Y. (2018). Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking. IEEE Network, 32(6), 35-41. doi:10.1109/mnet.2018.1800097Lin, Y. (2002). Data Mining and Knowledge Discovery, 6(3), 259-275. doi:10.1023/a:1015469627679Lopez-Martin, M., Carro, B., Lloret, J., Egea, S., & Sanchez-Esguevillas, A. (2018). Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets. IEEE Communications Magazine, 56(9), 110-117. doi:10.1109/mcom.2018.1701156Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. doi:10.1109/72.329697Nguyen, T. T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56-76. doi:10.1109/surv.2008.080406Queiroz, W., Capretz, M. A. M., & Dantas, M. (2019). An approach for SDN traffic monitoring based on big data techniques. Journal of Network and Computer Applications, 131, 28-39. doi:10.1016/j.jnca.2019.01.016Rego, A., Canovas, A., Jimenez, J. M., & Lloret, J. (2018). An Intelligent System for Video Surveillance in IoT Environments. IEEE Access, 6, 31580-31598. doi:10.1109/access.2018.2842034Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K. (2010). Study of Subjective and Objective Quality Assessment of Video. IEEE Transactions on Image Processing, 19(6), 1427-1441. doi:10.1109/tip.2010.2042111Soysal, M., & Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6), 451-467. doi:10.1016/j.peva.2010.01.001Tan, X., Xie, Y., Ma, H., Yu, S., & Hu, J. (2019). Recognizing the content types of network traffic based on a hybrid DNN-HMM model. Journal of Network and Computer Applications, 142, 51-62. doi:10.1016/j.jnca.2019.06.004Tongaonkar, A., Torres, R., Iliofotou, M., Keralapura, R., & Nucci, A. (2015). Towards self adaptive network traffic classification. Computer Communications, 56, 35-46. doi:10.1016/j.comcom.2014.03.02

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks

Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power, motivates us to improve the prediction accuracy via deep-learning approaches. Recurrent neural networks have become one of the most popular methods for time series forecasting, however, due to the variety of these networks, the question that which type is the most appropriate one for this task remains unsolved. In this paper, we use three kinds of recurrent neural networks including simple RNN units, GRU and LSTM neural network to predict short-term traffic flow. The dataset from TAP30 Corporation is used for building the models and comparing RNNs with several well-known models, such as DEMA, LASSO and XGBoost. The results show that all three types of RNNs outperform the others, however, more simple RNNs such as simple recurrent units and GRU perform work better than LSTM in terms of accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279, arXiv:1804.04176 by other author