On dynamic server provisioning in multichannel P2P live streaming

To guarantee the streaming quality in live peer-to-peer (P2P) streaming channels, it is preferable to provision adequate levels of upload capacities at dedicated streaming servers, compensating for peer instability and time-varying peer upload bandwidth availability. Most commercial P2P streaming systems have resorted to the practice of overprovisioning a fixed amount of upload capacity on streaming servers. In this paper, we have performed a detailed analysis on 10 months of run-time traces from UUSee, a commercial P2P streaming system, and observed that available server capacities are not able to keep up with the increasing demand by hundreds of channels. We propose a novel online server capacity provisioning algorithm that proactively adjusts server capacities available to each of the concurrent channels, such that the supply of server bandwidth in each channel dynamically adapts to the forecasted demand, taking into account the number of peers, the streaming quality, and the channel priority. The algorithm is able to learn over time, has full Internet service provider (ISP) awareness to maximally constrain P2P traffic within ISP boundaries, and can provide differentiated streaming qualities to different channels by manipulating their priorities. To evaluate its effectiveness, our experiments are based on an implementation of the algorithm, which replays real-world traces. © 2011 IEEE.published_or_final_versio

Dynamic Resource Management in Clouds: A Probabilistic Approach

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz/flash crowd effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note: substantial text overlap with arXiv:1209.515

LIVE STREAMING USING PEER DIVISION MULTIPLEXING

A Number of commercial peer-to-peer (P2P) systems for live streaming have been introduced in recent years. The behaviour of the popular systems has been extensively studied in several measurement papers. However, these studies have to rely on a “black-box” approach, where packet traces are collected from a single or a limited number of measurement points, to infer various properties of the traffic on the control and data planes. Although, such studies are useful to compared different systems from the end user’s perspective. It is difficult to intuitively understand the observed properties without fully reverseengineering the underlying systems. In this paper, we describe the network architecture of Zattoo, one of the largest production, live streaming providers, in Europe, at the time of writing, and present a large-scale measurement study of zattoo, using data collected by the provider. To highlight we found that even, when the zattoo system was heavily loaded with as high as 20000 concurrent users on a single overlay, the median channel join delay remained less than 2-5 s, and that, for a majority of users, the streamed signal lags over-the-air broadcast signal by more than 3 s

Mathematical analysis of scheduling policies in peer-to-peer video streaming networks

Las redes de pares son comunidades virtuales autogestionadas, desarrolladas en la capa de aplicación sobre la infraestructura de Internet, donde los usuarios (denominados pares) comparten recursos (ancho de banda, memoria, procesamiento) para alcanzar un fin común. La distribución de video representa la aplicación más desafiante, dadas las limitaciones de ancho de banda. Existen básicamente tres servicios de video. El más simple es la descarga, donde un conjunto de servidores posee el contenido original, y los usuarios deben descargar completamente este contenido previo a su reproducción. Un segundo servicio se denomina video bajo demanda, donde los pares se unen a una red virtual siempre que inicien una solicitud de un contenido de video, e inician una descarga progresiva en línea. El último servicio es video en vivo, donde el contenido de video es generado, distribuido y visualizado simultáneamente. En esta tesis se estudian aspectos de diseño para la distribución de video en vivo y bajo demanda. Se presenta un análisis matemático de estabilidad y capacidad de arquitecturas de distribución bajo demanda híbridas, asistidas por pares. Los pares inician descargas concurrentes de múltiples contenidos, y se desconectan cuando lo desean. Se predice la evolución esperada del sistema asumiendo proceso Poisson de arribos y egresos exponenciales, mediante un modelo determinístico de fluidos. Un sub-modelo de descargas secuenciales (no simultáneas) es globalmente y estructuralmente estable, independientemente de los parámetros de la red. Mediante la Ley de Little se determina el tiempo medio de residencia de usuarios en un sistema bajo demanda secuencial estacionario. Se demuestra teóricamente que la filosofía híbrida de cooperación entre pares siempre desempeña mejor que la tecnología pura basada en cliente-servidor

Un modèle de trafic adapté à la volatilité de charge d'un service de vidéo à la demande: Identification, validation et application à la gestion dynamique de ressources.

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this report we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. As an essential step we also derive a heuristic identification procedure to calibrate all the model parameters and evaluate the performance of our estimator on synthetic time series. We show how good can our model fit to real workload traces with respect to the stationary case in terms of steady-state probability and autocorrelation structure. We find that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.La gestion dynamique de ressources est un élément clé du paradigme de cloud computing et plus récemment de celui de cloud networking. Dans ce contexte d'infrastructures virtualisées, la réduction des coûts associés à l'utilisation et à la ré-allocation des ressources contraint les opé- rateurs et les utilisateurs de clouds à une gestion rationnelle de celles-ci. Dans ce travail nous proposons une description probabiliste des besoins liée à la volatilité de la charge d'un service de distribution de vidéos à la demande. Cette description peut alors servir de consigne (input) à la provision et à l'allocation dynamique des ressources nécessaires. Notre approche repose sur la construction d'un modèle stochastique inspiré des modèles de Markov standards de propaga- tion épidémiologique, capable de reproduire des variations soudaines et intenses d'activité (buzz). Nous proposons alors une procédure heuristique d'identification du modèle à partir de séries tem- porelles du nombre d'utilisateurs connectés au serveur. Les performances d'estimation de chacun des paramètres du modèle sont évaluées numériquement, et nous vérifions l'adéquation du modèle aux données en comparant les distributions des états stationnaires ainsi que les fonctions d'auto- corrélation des processus. Les propriétés markoviennes de notre modèle garantissent qu'il vérifie un principe de grandes dé- viations permettant de caractériser statistiquement l'ampleur et la durée d'évènements extrêmes et rares tels que ceux produits par les buzzs. C'est cette propriété que nous exploitons pour di- mensionner le volume de ressources (e.g. bande-passante, nombre de serveurs, taille de buffers) à prévoir pour réaliser un bon compromis entre coût de re-déploiement des infrastructures et qualité de service. Cette approche probabiliste de la gestion des ressources ouvre des perspectives sur les politiques de Service Level Agreement adaptées aux clouds et servant au mieux les intérêts des opérateurs de réseaux, de services et de leurs clients