Redundant VoD Streaming Service in a Private Cloud: Availability Modeling and Sensitivity Analysis

For several years cloud computing has been generating considerable debate and interest within IT corporations. Since cloud computing environments provide storage and processing systems that are adaptable, efficient, and straightforward, thereby enabling rapid infrastructure modifications to be made according to constantly varying workloads, organizations of every size and type are migrating to web-based cloud supported solutions. Due to the advantages of the pay-per-use model and scalability factors, current video on demand (VoD) streaming services rely heavily on cloud infrastructures to offer a large variety of multimedia content. Recent well documented failure events in commercial VoD services have demonstrated the fundamental importance of maintaining high availability in cloud computing infrastructures, and hierarchical modeling has proved to be a useful tool for evaluating the availability of complex systems and services. This paper presents an availability model for a video streaming service deployed in a private cloud environment which includes redundancy mechanisms in the infrastructure. Differential sensitivity analysis was applied to identify and rank the critical components of the system with respect to service availability. The results demonstrate that such a modeling strategy combined with differential sensitivity analysis can be an attractive methodology for identifying which components should be supported with redundancy in order to consciously increase system dependability

Noozy AI Development of a recommender system for video-on-demand platform

Recommender algorithms can guide users in a personalized way to interesting objects in a large space of possible options. The necessity of recommendations is increasing on cultural or entertainment and media industries, where the number of products is continuously increasing. Cultural and media platforms and digital markets are getting heavily benefitted from implementing, maintaining, and improving their recommender system. The process on how they retrieve cultural products like music, books, movies, news and enable easy access to the users can have a structural impact on how markets operate alongside how consuming trends change. The prospect of this project is to engineer a complete recommender system for noozy.tv, a new video-on-demand platform dedicated for the viewers of Grand Est region in France. The aim is to develop a framework maintaining standard and modern software development methodologies and tools to ensure seamless service, research scope on real data and diversity, evaluation, and delivering a platform for further improvement in system.Résumé Les algorithmes de recommandation peuvent guider les utilisateurs de manière personnalisée vers des objets intéressants dans un large espace d'options possibles. La nécessité de recommandations augmente sur les industries culturelles ou du divertissement et des médias, où le nombre de produits ne cesse d'augmenter. Les plateformes culturelles et médiatiques et les marchés numériques bénéficient grandement de la mise en oeuvre, de la maintenance et de l'amélioration de leur système de recommandation. Le processus sur la façon dont ils récupèrent les produits culturels comme la musique, les livres, les films, les actualités et permettent un accès facile aux utilisateurs peut avoir un impact structurel sur le fonctionnement des marchés parallèlement à l'évolution des tendances de consommation. La perspective de ce projet est de concevoir un système de recommandation complet pour noozy.tv, une nouvelle plateforme de vidéo à la demande dédiée aux téléspectateurs de la région Grand Est en France. L'objectif est de développer un cadre maintenant des méthodologies et des outils de développement de logiciels standard et modernes pour assurer un service transparent, une portée de recherche sur les données réelles et la diversité, l'évaluation et la fourniture d'une plate-forme pour une amélioration supplémentaire du système

Enhancement Security in Smart TV Web Application

During the course of its research, the security firmware of the TV's Internet interface failed to confirm script integrity before scripts were run. The attacker could intercept transmissions from the television to the network using common DNS, DHCP server, and TCP session hijacking techniques. The code could then be injected into the normal DataStream, allowing attackers to obtain total control over the device's Internet functionality. This attack could render the product unusable at important times and extend or limit its functionality without the manufacturer's permission. More importantly, however, this same mechanism could be used to extract sensitive credentials from the TV's memory, or prompt the user to fill out fake online forms to capture credit card information. Additionally, Hackers were able to recover the manufacturer's private third-party developer keys†from the television, because in many cases, these keys were transmitted unencrypted and in the clear. Many third-party searches, music, video and photo-sharing services delivered over the Internet require such keys, and a big TV Manufacturer often purchases high-volume special access privileges to these service provider's networks. A hacker could potentially employ these keys, for example, to access these high-volume services at no charge. This paper describes the new Authentication mechanism for online transaction payment for more secured service and, analyzing network managed challenge to avoid the vulnerabilities

SHStream: Self-Healing Framework for HTTP Video-Streaming

HTTP video-streaming is leading delivery of video content over the Internet. This phenomenon is explained by the ubiquity of web browsers, the permeability of HTTP traffic and the recent video technologies around HTML5. However, the inclusion of multimedia requests imposes new requirements on web servers due to responses with lifespans that can reach dozens of minutes and timing requirements for data fragments transmitted during the response period. Consequently, web- servers require real-time performance control to avoid playback outages caused by overloading and performance anomalies. We present SHStream , a self-healing framework for web servers delivering video-streaming content that provides (1) load admit- tance to avoid server overloading; (2) prediction of performance anomalies using online data stream learning algorithms; (3) continuous evaluation and selection of the best algorithm for prediction; and (4) proactive recovery by migrating the server to other hosts using container-based virtualization techniques. Evaluation of our framework using several variants of Hoeffding trees and ensemble algorithms showed that with a small number of learning instances, it is possible to achieve approximately 98% of recall and 99% of precision for failure predictions. Additionally, proactive failover can be performed in less than 1 secon

Flexible media transport framework based on service composition for future network

This work introduces common guidelines defined in several standardization organisms towards future networks based on the actual mechanisms and protocols used to treat the multimedia data, most of them placed in the application layer of the OSI reference model.Peer ReviewedPreprin

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Diseño centrado en calidad para la difusión Peer-to-Peer de video en vivo

El uso de redes Peer-to-Peer (P2P) es una forma escalable para ofrecer servicios de video sobre Internet. Este documento hace foco en la definición, desarrollo y evaluación de una arquitectura P2P para distribuir video en vivo. El diseño global de la red es guiado por la calidad de experiencia (Quality of Experience - QoE), cuyo principal componente en este caso es la calidad del video percibida por los usuarios finales, en lugar del tradicional diseño basado en la calidad de servicio (Quality of Service - QoE) de la mayoría de los sistemas. Para medir la calidad percibida del video, en tiempo real y automáticamente, extendimos la recientemente propuesta metodología Pseudo-Subjective Quality Assessment (PSQA). Dos grandes líneas de investigación son desarrolladas. Primero, proponemos una técnica de distribución de video desde múltiples fuentes con las características de poder ser optimizada para maximizar la calidad percibida en contextos de muchas fallas y de poseer muy baja señalización (a diferencia de los sistemas existentes). Desarrollamos una metodología, basada en PSQA, que nos permite un control fino sobre la forma en que la señal de video es dividida en partes y la cantidad de redundancia agregada, como una función de la dinámica de los usuarios de la red. De esta forma es posible mejorar la robustez del sistema tanto como sea deseado, contemplando el límite de capacidad en la comunicación. En segundo lugar, presentamos un mecanismo estructurado para controlar la topología de la red. La selección de que usuarios servirán a que otros es importante para la robustez de la red, especialmente cuando los usuarios son heterogéneos en sus capacidades y en sus tiempos de conexión.Nuestro diseño maximiza la calidad global esperada (evaluada usando PSQA), seleccionado una topología que mejora la robustez del sistema. Además estudiamos como extender la red con dos servicios complementarios: el video bajo demanda (Video on Demand - VoD) y el servicio MyTV. El desafío en estos servicios es como realizar búsquedas eficientes sobre la librería de videos, dado al alto dinamismo del contenido. Presentamos una estrategia de "caching" para las búsquedas en estos servicios, que maximiza el número total de respuestas correctas a las consultas, considerando una dinámica particular en los contenidos y restricciones de ancho de banda. Nuestro diseño global considera escenarios reales, donde los casos de prueba y los parámetros de configuración surgen de datos reales de un servicio de referencia en producción. Nuestro prototipo es completamente funcional, de uso gratuito, y basado en tecnologías bien probadas de código abierto