Control evaluation in a LVoD system based on a peer-to-peer multicast scheme

Providing Quality of Service (QoS) in video on demand systems (VoD) is a challenging problem. In this paper, we analyse the fault tolerance on a P2P multicast delivery scheme, called Patch Collaboration Manager / Multicast Channel Distributed Branching (PCM/MCDB) [13]. This scheme decentralizes the delivery process between clients and scales the VoD server performance. PCM/MCDB synchronizes a group of clients in order to create local network channels to replace on-going multicast channels from the VoD server. Using the P2P paradigm supposes facing the challenge of how often peers connect and disconnect from the system. To address this problem, a centralized mechanism is able to replace the failed client. We evaluate the failure management process of the centralized scheme in terms of the overhead injected into the network and analyse the applicability of a distributed approach to managing the process. Analytical models are developed for centralized and distributed approaches. Their behaviour are compared in order to evaluate whether the distributed scheme can improve the fault management process, in terms of reducing server load and generating better scalability.Proporcionar Calidad de Servicio (QoS) en sistemas de Vídeo bajo Demanda (VoD) es un problema desafiador. En este artículo, analizamos la tolerancia a fallos en un esquema de envío de informaciones, basado en comunicaciones multicast y colaboraciones P2P, denominado PCM/MCDB [13]. El esquema descentraliza el proceso de envío de información entre los clientes y escala las prestaciones del servidor de VoD. PCM/MCDB sincroniza un grupo de clientes con objeto de crear canales de redes locales para reemplazar canales multicast en curso del servidor. La aplicación del paradigma P2P supone cómo afrontar el problema de la conexión y desconexión de clientes del sistema. Para resolver este problema, un mecanismo centralizado es capaz de reemplazar el cliente fallido. En el trabajo evaluamos el proceso de gestión de fallos del esquema centralizado en términos del flujo de informaciones insertado en la red y analizamos la aplicabilidad de un esquema distribuido para el proceso de gestión. Modelos analíticos son desarrollados para las aproximaciones centralizada y distribuida. Sus comportamientos son comparados con objeto de evaluar si un esquema distribuido puede mejorar el proceso de gestión de fallos desde el punto de vista de reducir la carga del servidor y proporcionar mejor escalabilidad.VIII Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI

Control evaluation in a LVoD system based on a peer-to-peer multicast scheme

Providing Quality of Service (QoS) in video on demand systems (VoD) is a challenging problem. In this paper, we analyse the fault tolerance on a P2P multicast delivery scheme, called Patch Collaboration Manager / Multicast Channel Distributed Branching (PCM/MCDB) [13]. This scheme decentralizes the delivery process between clients and scales the VoD server performance. PCM/MCDB synchronizes a group of clients in order to create local network channels to replace on-going multicast channels from the VoD server. Using the P2P paradigm supposes facing the challenge of how often peers connect and disconnect from the system. To address this problem, a centralized mechanism is able to replace the failed client. We evaluate the failure management process of the centralized scheme in terms of the overhead injected into the network and analyse the applicability of a distributed approach to managing the process. Analytical models are developed for centralized and distributed approaches. Their behaviour are compared in order to evaluate whether the distributed scheme can improve the fault management process, in terms of reducing server load and generating better scalability.Proporcionar Calidad de Servicio (QoS) en sistemas de Vídeo bajo Demanda (VoD) es un problema desafiador. En este artículo, analizamos la tolerancia a fallos en un esquema de envío de informaciones, basado en comunicaciones multicast y colaboraciones P2P, denominado PCM/MCDB [13]. El esquema descentraliza el proceso de envío de información entre los clientes y escala las prestaciones del servidor de VoD. PCM/MCDB sincroniza un grupo de clientes con objeto de crear canales de redes locales para reemplazar canales multicast en curso del servidor. La aplicación del paradigma P2P supone cómo afrontar el problema de la conexión y desconexión de clientes del sistema. Para resolver este problema, un mecanismo centralizado es capaz de reemplazar el cliente fallido. En el trabajo evaluamos el proceso de gestión de fallos del esquema centralizado en términos del flujo de informaciones insertado en la red y analizamos la aplicabilidad de un esquema distribuido para el proceso de gestión. Modelos analíticos son desarrollados para las aproximaciones centralizada y distribuida. Sus comportamientos son comparados con objeto de evaluar si un esquema distribuido puede mejorar el proceso de gestión de fallos desde el punto de vista de reducir la carga del servidor y proporcionar mejor escalabilidad.VIII Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI

An Integrated Quality-of-Service Model for Video-on-Demand Application.

The tremendous growth of the Internet paradigm has given rise to Quality of Service (QoS) problems in heterogeneous, ubiquitous, distributed real time applications such as video-on-Demand (VoD). The challenging task in VoD applications is to satisfy diverse client requests for discrete videos with restrained resources by invoking versatile QoS schemes. In this paper, a hybrid QoS strategy, which is a combination of batching and recursive patching is implemented in the local server to ensure starvation-free resource management thereby enhancing the throughput. Batching shares network resources efficiently whereas recursive patching is adopted to reduce the time difference between the requests. The suggested algorithm delivers the complete video to the users based on one of the three communication channels: broadcast, multicast and unicast depending on whether the video is very popular, average popular and least popular respectively. The experimental results show that our strategy accomplishes 35% - 40% reduction in terms of blocking ratio and throughput is 10% - 15% higher than the Poon’s strategy, which guarantees that not only the resources are efficiently utilized but also a suitable Quality of Service is provided to each user

Maximizing Resource Utilization In Video Streaming Systems

Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to utilize include server bandwidth, network bandwidth, battery life in battery operated devices, and processing time in limited processing power devices. In this work, we propose new techniques to maximize the utilization of video-on-demand (VOD) server resources. In addition to that, we propose new framework to maximize the utilization of the network bandwidth in wireless video streaming systems. Providing video streaming users in a VOD system with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait thereby increasing server utilization by increasing server throughput. In this work, we analyze waiting-time predictability in scalable video streaming. We also propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. The achieved resource sharing by stream merging depends greatly on how the waiting requests are scheduled for service. Motivated by the development of cost-based scheduling, we investigate its effectiveness in great detail and discuss opportunities for further tunings and enhancements. Additionally, we analyze the effectiveness of incorporating video prediction results into the scheduling decisions. We also study the interaction between scheduling policies and the stream merging techniques and explore new ways for enhancements. The interest in video surveillance systems has grown dramatically during the last decade. Auto-mated video surveillance (AVS) serves as an efficient approach for the realtime detection of threats and for monitoring their progress. Wireless networks in AVS systems have limited available bandwidth that have to be estimated accurately and distributed efficiently. In this research, we develop two cross-layer optimization frameworks that maximize the bandwidth optimization of 802.11 wireless network. We develop a distortion-based cross-layer optimization framework that manages bandwidth in the wire-less network in such a way that minimizes the overall distortion. We also develop an accuracy-based cross-layer optimization framework in which the overall detection accuracy of the computer vision algorithm(s) running in the system is maximized. Both proposed frameworks manage the application rates and transmission opportunities of various video sources based on the dynamic network conditions to achieve their goals. Each framework utilizes a novel online approach for estimating the effective airtime of the network. Moreover, we propose a bandwidth pruning mechanism that can be used with the accuracy-based framework to achieve any desired tradeoff between detection accuracy and power consumption. We demonstrate the effectiveness of the proposed frameworks, including the effective air-time estimation algorithms and the bandwidth pruning mechanism, through extensive experiments using OPNET

A Video On Demand System Architecture For Heterogeneous Mobile Ad Hoc Networks For Different Devices.

this paper proposed new system architecture for Mobile Ad Hoc Networks (MANETs) on heterogeneous network to provide optimal Video on Demand (VoD) services to difference types of devices with optimal bandwidth utilization

Design and performance analysis of a super-scalar video-on-demand system.

Lee Chung Hing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 61-63).Abstracts in English and Chinese.Acknowledgements --- p.iiAbstract --- p.iiiList of Figures --- p.viiChapter 1. --- Introduction --- p.1Chapter 1.1 --- Contributions of This Thesis --- p.3Chapter 1.2 --- Organizations of This Thesis --- p.3Chapter 1.3 --- Publication --- p.4Chapter 2. --- Overview of VoD Systems --- p.5Chapter 2.1 --- True VoD --- p.6Chapter 2.2 --- Near VoD --- p.7Chapter 2.3 --- Related Works --- p.9Chapter 2.3.1 --- Batching --- p.9Chapter 2.3.2 --- Patching --- p.11Chapter 2.3.3 --- Mcache --- p.11Chapter 2.3.4 --- Unified VoD --- p.12Chapter 2.4 --- Discussions --- p.15Chapter 3. --- Super-Scalar Architecture --- p.17Chapter 3.1 --- Transmission Scheduling --- p.20Chapter 3.2 --- Admission Control --- p.21Chapter 3.3 --- Channel Merging --- p.26Chapter 3.4 --- Interactive Control --- p.29Chapter 4. --- Performance Modeling --- p.31Chapter 4.1 --- Waiting Time for Statically-Admitted Clients --- p.32Chapter 4.2 --- Waiting Time for Dynamically-Admitted Clients --- p.33Chapter 4.3 --- Admission Threshold --- p.38Chapter 4.4 --- Channel Partitioning --- p.39Chapter 5. --- Performance Evaluation --- p.40Chapter 5.1 --- Model Validation --- p.40Chapter 5.2 --- Channel Partitioning --- p.42Chapter 5.3 --- Latency Comparisons --- p.44Chapter 5.4 --- Channel Requirement --- p.46Chapter 5.5 --- Performance at Light Loads --- p.47Chapter 5.6 --- Multiplexing Gain --- p.49Chapter 6. --- Implementation and Benchmarking --- p.51Chapter 6.1 --- Implementation Description --- p.51Chapter 6.2 --- Benchmarking --- p.53Chapter 6.2.1 --- Benchmarking Setup --- p.53Chapter 6.2.2 --- Benchmarking Result --- p.55Chapter 7. --- Conclusion --- p.56Appendix --- p.57Bibliography --- p.6