3,731 research outputs found

    Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets

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    [EN] Quality of experience (QoE) is the overall acceptability of an application or service, as perceived subjectively by the end user. In particular, for video quality the QoE is dependent of video transmission parameters. To monitor and control these parameters is critical in modern network management systems, but it would be better to be able to monitor the QoE itself (in terms of both interpretation and accuracy) rather than the parameters on which it depends. In this article we present the first attempt to predict video QoE based on information directly extracted from the network packets using a deep learning model. The QoE detector is based on a binary classifier (good or bad quality) for seven common classes of anomalies when watching videos (blur, ghost, etc.). Our classifier can detect anomalies at the current time instant and predict them at the next immediate instant. This classifier faces two major challenges: first, a highly unbalanced dataset with a low proportion of samples with video anomaly, and second, a small amount of training data, since it must be obtained from individual viewers under a controlled experimental setup. The proposed classifier is based on a combination of a convolutional neural network (CNN), recurrent neural network, and Gaussian process classifier. Image processing, which is the common domain for a CNN, has been expanded to QoE detection. Based on a detailed comparison, the proposed model offers better performance metrics than alternative machine learning algorithms, and can be used as a QoE monitoring function in edge computingThis work has been funded by the Ministerio de Economia y Competitividad del Gobierno de Espana and the Fondo de Desarrollo Regional (FEDER) within the project "Inteligencia distribuida para el control y adaptacion de redes dinamicas definidas por software, Ref: TIN2014-57991-C3-2-P," and also 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 with the projects "Distribucion inteligente de servicios multimedia utilizando redes cognitivas adaptativas definidas por software, Ref: TIN2014-57991-C3-1-P" and "Red Cognitiva Definida por Software Para Optimizar y Securizar Trafico de Internet de las Cosas con Informacion Critica, Ref TIN2017-84802-C2-1-P."Lopez-Martin, M.; Carro, B.; Lloret, J.; Egea, S.; SĂĄnchez-Esguevillas, A. (2018). Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets. IEEE Communications Magazine. 56(9):110-117. https://doi.org/10.1109/MCOM.2018.170115611011756

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

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    [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.2019.102498S114150Cånovas, A., Taha, M., Lloret, J., & Tomås, 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., & SchÀfer, 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

    Deep Reinforcement Learning for Resource Management in Network Slicing

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    Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

    Improving The Efficiency Of Video Transmission In Computer Networks

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    In-depth examination of current techniques for enhancing the efficiency of video transmission over digital networks is provided in this study. Due to the growing need for high-quality video content, optimizing video transmission is an important area of research. This review categorizes and in-depth examines a range of methods proposed in the literature to enhance video transmission effectiveness. ABR, DNN architecture, adaptive streaming, Quality of Service (QoS), error resilience, congestion control, video compression, and hardware acceleration for video provisioning are just a few of the cutting-edge techniques that are covered in the discussion, which ranges from the more traditional to the cutting-edge. This essay provides a methodical evaluation of the numerous tactics that are available, along with an analysis of their guiding principles, advantages, and disadvantages. The paper also offers a comparative analysis of various approaches, highlighting trends, gaps, and potential future research directions in this crucial domain, all of which help to create more efficient video compression and transmission paradigms in computer networks

    A traffic classification method using machine learning algorithm

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    Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

    A telecom analytics framework for dynamic quality of service management

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    Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications

    Real-time Bandwidth Estimation from Offline Expert Demonstrations

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    In this work, we tackle the problem of bandwidth estimation (BWE) for real-time communication systems; however, in contrast to previous works, we leverage the vast efforts of prior heuristic-based BWE methods and synergize these approaches with deep learning-based techniques. Our work addresses challenges in generalizing to unseen network dynamics and extracting rich representations from prior experience, two key challenges in integrating data-driven bandwidth estimators into real-time systems. To that end, we propose Merlin, the first purely offline, data-driven solution to BWE that harnesses prior heuristic-based methods to extract an expert BWE policy. Through a series of experiments, we demonstrate that Merlin surpasses state-of-the-art heuristic-based and deep learning-based bandwidth estimators in terms of objective quality of experience metrics while generalizing beyond the offline world to in-the-wild network deployments where Merlin achieves a 42.85% and 12.8% reduction in packet loss and delay, respectively, when compared against WebRTC in inter-continental videoconferencing calls. We hope that Merlin's offline-oriented design fosters new strategies for real-time network control

    A cross-layer approach to enhance QoS for multimedia applications over satellite

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    The need for on-demand QoS support for communications over satellite is of primary importance for distributed multimedia applications. This is particularly true for the return link which is often a bottleneck due to the large set of end-users accessing a very limited uplink resource. Facing this need, Demand Assignment Multiple Access (DAMA) is a classical technique that allows satellite operators to offer various types of services, while managing the resources of the satellite system efficiently. Tackling the quality degradation and delay accumulation issues that can result from the use of these techniques, this paper proposes an instantiation of the Application Layer Framing (ALF) approach, using a cross-layer interpreter(xQoS-Interpreter). The information provided by this interpreter is used to manage the resource provided to a terminal by the satellite system in order to improve the quality of multimedia presentations from the end users point of view. Several experiments are carried out for different loads on the return link. Their impact on QoS is measured through different application as well as network level metrics
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