An SDN-approach for QoE management of multimedia services using resource allocation

Future networks will be accompanied by new heterogeneous requirements in terms of end-users Quality of Experience (QoE) due to the increasing number of application scenarios being deployed. Network softwarization technologies such as Software Defined Networks (SDNs) and Network Function Virtualization (NFV) promise to provide these capabilities. In this paper, a novel QoE-driven resource allocation mechanism is proposed to dynamically assign tasks to virtual network nodes in order to achieve an optimized end-to-end quality. The aim is to find the best combination of network node functions that can provide an optimized level of QoE to the end users though node cooperation. The service in question is divided in tasks and the neighbor nodes negotiate the assignment of these considering the final quality. In the paper we specifically focus on the video streaming service. We also show that the agility provided by SDN/NFV is a key factor for enhancing video quality, resource allocation and QoE management in future networks. Preliminary results based on the Mininet network emulator and the OpenDaylight controller have shown that our approach can significantly improve the quality of a transmitted video by selecting the best path with normalized QoS values

Quality of Experience monitoring and management strategies for future smart networks

One of the major driving forces of the service and network's provider market is the user's perceived service quality and expectations, which are referred to as user's Quality of Experience (QoE). It is evident that QoE is particularly critical for network providers, who are challenged with the multimedia engineering problems (e.g. processing, compression) typical of traditional networks. They need to have the right QoE monitoring and management mechanisms to have a significant impact on their budget (e.g. by reducing the users‘ churn). Moreover, due to the rapid growth of mobile networks and multimedia services, it is crucial for Internet Service Providers (ISPs) to accurately monitor and manage the QoE for the delivered services and at the same time keep the computational resources and the power consumption at low levels. The objective of this thesis is to investigate the issue of QoE monitoring and management for future networks. This research, developed during the PhD programme, aims to describe the State-of-the-Art and the concept of Virtual Probes (vProbes). Then, I proposed a QoE monitoring and management solution, two Agent-based solutions for QoE monitoring in LTE-Advanced networks, a QoE monitoring solution for multimedia services in 5G networks and an SDN-based approach for QoE management of multimedia services

Adapting reinforcement learning for multimedia transmission on SDN

[EN] Multimedia transmissions require a high quantity of resources to ensure their quality. In the last years, some technologies that provide a better resource management have appeared. Software defined networks (SDNs) are presented as a solution to improve this management. Furthermore, combining SDN with artificial intelligence (AI) techniques, networks are able to provide a higher performance using the same resources. In this paper, a redefinition of reinforcement learning is proposed. This model is focused on multimedia transmission in a SDN environment. Moreover, the architecture needed and the algorithm of the reinforcement learning are described. Using the Openflow protocol, several sample actions are defined in the system. Results show that using the system users perceive an increase in the image quality three times better. Moreover, the loss rate is reduced more than half the value of losses recorded when the algorithm is not applied. Regarding bandwidth, the maximum throughput increases from 987.16 kbps to 24.73 Mbps while the average bandwidth improves from 412.42 kbps to 7.83 Mbps.Ayudas para contratos predoctorales de Formación del Profesorado Universitario FPU (Convocatoria 2015), Grant/Award Number: FPU15/06837; Programa Estatal de Investigación Científica y Técnica de Excelencia (Convocatoria 2017), Grant/Award Number: TIN2017-84802-C2-1-P; Programa Estatal De Investigación, Desarrollo e Innovación Orientada a los retos de la sociedad (Convocatoria 2016), Grant/Award Number: TEC2016-76795-C6-4-R; ERANETMED, Grant/Award Number: ERANETMED3-227 SMARTWATIRRego Mañez, A.; Sendra, S.; García-García, L.; Lloret, J. (2019). Adapting reinforcement learning for multimedia transmission on SDN. Transactions on Emerging Telecommunications Technologies. 30(9):1-15. https://doi.org/10.1002/ett.3643S11530

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks

Modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicio de telecomunicaciones OTT-Telco

ANTECEDENTES El aprovisionamiento de la Calidad de la Experiencia (QoE) en servicios de telecomunicaciones requiere de sistemas de gestión que permitan monitorizar y controlar la QoE de los usuarios luego de consumir diferentes servicios de internet provistos sobre la red del operador. En efecto, el consumo elevado de datos por parte de los usuarios requiere, a nivel de gestión de la red, la asignación de recursos suficientes para el correcto funcionamiento de los servicios. En particular, la configuración de la Calidad del Servicio (QoS) ofrecida por el operador dentro de su dominio de operación se torna fundamental para proveer un tratamiento apropiado del tráfico, permitiendo que la percepción de la calidad del servicio por parte de los usuarios finales pueda mantenerse dentro del umbral de tolerancia de acuerdo con las políticas establecidas por la compañía de telecomunicaciones (Telco). En consecuencia, un modelo de correlación QoS-QoE es clave en el aprovisionamiento de servicios de internet sobre la infraestructura del operador de telecomunicaciones. OBJETIVOS La presente tesis de doctorado se centra en proponer un modelo de correlación QoS-QoE en un ambiente de aprovisionamiento de servicios de telecomunicaciones OTT-Telco. Para ello, cinco acciones generales deben llevarse a cabo; a saber: () caracterizar los parámetros de QoS que mayor efecto tienen en la degradación de servicios OTT. () determinar las características, condiciones, parámetros y medidas de QoE en la prestación de un servicio OTT. () establecer las condiciones y restricciones de prestación de un servicio OTT en la infraestructura de una Telco que mantenga una buena relación QoS-QoE. () desarrollar un mecanismo de estimación o predicción de QoE con base en los factores de influencia de QoS que afectan la prestación de un servicio OTT. () evaluar experimentalmente el modelo de correlación QoE-QoS. MÉTODOS Para el cumplimiento de los objetivos, se definió un modelo integrado por un macro-componente Conceptualización y otro Operacional. El macro-componente Conceptualización está orientado por el referente metodológico para la construcción de marcos conceptuales de Jabareen, y el macro-componente Operacional está alineado con las fases definidas para el desarrollo de proyectos de minería de datos, CRISP-DM. Adicionalmente, se emplearon diseños de comprobación para los algoritmos, con el fin de comprobar la validez del modelo de estimación basado en algoritmos de aprendizaje automático; es decir, el modelo de estimación fue evaluado a partir de un diseño de comprobación donde se definen, para cada uno de los algoritmos, los parámetros iniciales de operación, las configuraciones de las diferentes pruebas, y las métricas usadas para evaluar su desempeño. RESULTADOS Los resultados más importantes alcanzados son los siguientes: un mapa estratégico del estado de la ciencia en el aprovisionamiento de la QoE para servicios OTT, una conceptualización de los perfiles del modelo de correlación, un modelo matemático para la valoración de la QoE de acuerdo con el comportamiento de consumo de los usuarios, un conjunto de datos de tráfico etiquetado que relaciona el comportamiento de la red con la percepción de la calidad de los usuarios, y un modelo de estimación de la QoE de los usuarios a partir del comportamiento de tráfico de la red. CONCLUSIONES El modelo de correlación QoS-QoE puede ser empleado en sistemas gestión de la QoE donde se requiere por parte de la Telco un diagnóstico y monitorización más objetiva de la percepción de la calidad del servicio por parte de sus usuarios dentro su red de aprovisionamiento. De igual manera, el empleo de parámetros adicionales de contexto de usuario enriquecería los sistemas de gestión de la QoE en el aprovisionamiento de servicios OTT.BACKGROUND Quality of Experience (QoE) provisioning requires robust QoE-centric network and application management on Telco network for providing internet services. Indeed, traffic growth over Telco network demands resource allocation for service well performance. Particularly, Quality of Service (QoS) configuration offered by network provider operational domain becomes a key component for traffic control in a proper manner. Hence, the quality of services perceived can be managed within a tolerance threshold according to telecom operator policies. Therefore, a QoS-QoE correlational model for internet services provisioning over the telecom operator infrastructure is required. AIMS The doctoral thesis is focused on propose a correlation QoS-QoE model for provisioning telecommunications services in OTT-Telco context. To this end, five goals must be accomplishing. () To characterize QoS parameters that more impact have on OTT services performance. () To determinate QoE assumptions, features, parameters, and metrics for OTT service provisioning. () To establish the assumptions and restrictions for providing a well QoS-QoE relation in the telecom operator. () To develop an estimation model for QoE based on QoS factors in the OTT services provisioning. () To evaluate the correlation QoS-QoE model. METHODS To accomplish the aims, a model with a Conceptual and Operational macro-component was structured. The Conceptual macro-component is based on the principles for building conceptual frameworks by Jabareen, and an Operational macro-component aligned with data mining project development phases, CRISP-DM. Furthermore, test bed design was structured to validate the estimation model base on machine learning algorithms; namely, algorithms initial parameters, some tests setup, and regression metrics were determined on a test bed for validate the performance of the estimation model proposed RESULTS The most relevant results achieved are the following: a strategic science map in the QoE provisioning for OTT services, three conceptual profiles as part of the correlation QoS-QoE model, a mathematical model for QoE assessment according to user consumption behavior, a label traffic dataset that relates the traffic network with quality of services perception, and estimation QoE model for users based on traffic flows. CONCLUSIONS The QoS-QoE correlational model can be applied in QoE-Driven application and network management in which an objective controlling and monitoring of quality of services perception by users is required. Moreover, additional user context parameters could be taking account for improving the QoE management systems in OTT services provisioning.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Jesús García Herrero.- Secretario: José Armando Ordóñez Córdoba.- Vocal: Juan Carlos Cuéllar Quiñóne