rue or interactive video-on-demand (iVoD) dedicates a single channel to each user and enables the video to be started at any time with VCR-like controls (pause, rewind, fast-forward, etc.). Today, IP network based deployments for services such as iVoD are very limited in scope. The largest deployed libraries achieve a mere 0.7 percent (5,000 hours) of the global movie and TV-series catalog (DVD by mail services, such as Netflix, offer up to 10 percent), and peak utilization reaches only 10-15 percent of broadcast TV. There is a strong belief among telecommunication companies that this market will expand exponentially in the next few years [1]. Service providers are intensely interested in scalable design methodologies for the deployment of large-scale IP content delivery networks that provide content propagation, storage, streaming, and transport.Extensive video-on-demand (VoD) systems have the potential to consume enormous bandwidth. The interactivity requirement and lack of network support restrict the widespread application of multicast streaming. Therefore, many concurrent unicast streams must be supported. Even when state-of-the-art compression methods such as MPEG-4 are employed, hundreds of gigabits of streaming capacity are required. The high bandwidth requirements encourage distributed architectures with replication of content and localization of network traffic, but such architectures imply a substantial increase in storage requirements, which is not a negligible factor, given the large size of video files.Many existing design tools ignore existing network infrastructure and focus on greenfield deployments, a situation that is rare in practice. There is an urgent need for design tools that can determine the best way to expand upon existing network infrastructure and can determine where to deploy storage and streaming devices to support the additional demand imposed by VoD systems. The tools must generate scalable architectures that can expand incrementally to handle orderof-magnitude increases in library size, rate of ingestion of new content, and peak demand.In this article, we outline the components of VoD network architectures and survey the current approaches to the design of these components. For each aspect of the VoD design challenge, we identify issues that are yet to be addressed or have received insufficient attention. Some of the key open research questions we raise are the following:• How should peer-to-peer content exchange operate in VoD systems and how should it affect pricing mechanisms? • How should VoD network design methods take into account the long tail of content effects that emerge as libraries and user-groups expand? • How can network designs meet current demands at a low cost yet remain suitable for expansion? • How can a VoD network adapt to the changing patterns of user behavior and the evolutions in the nature of accessed content?We present the architectures and topologies that have been considered for content distribution (delivery) networks (CDNs) and VoD deployments, as w...

The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; iv) almost all tools use ad-hoc techniques to address measurement noise; and v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead compared to existing approaches.

This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies a number of desired output (label/response) constraints. LSAT problems naturally arise in many applications in which one is interested in the class of inputs that produce desirable outputs, rather than simply a single optimum. A distinctive aspect of LSAT problems is that the output behavior is assessed only on the solution set, whereas in most statistical learning problems output behavior is evaluated over the entire input space. We present a novel support vector machine (SVM) algorithm for solving LSAT problems and apply it to a synthetic data set to illustrate the impact of the LSAT formulation.

Video-on-Demand (VoD) services are very user-friendly, but also complex and resource demanding. Deployments involve careful design of many mechanisms where content attributes and usage models should be taken into account. We define, and propose a methodology to solve, the VoD Equipment Allocation Problem of determining the number and type of streaming servers with directly attached storage (VoD servers) to install at each potential location in a metropolitan area network topology such that deployment costs are minimized. We develop a cost model for VoD deployments based on streaming, storage and transport costs and train a parametric function that maps the amount of available storage to a worst-case hit ratio. We observe the impact of having to determine the amount of storage and streaming cojointly, and determine the minimum demand required to deploy replicas as well as the average hit ratio at each location. We observe that common video-on-demand server configurations lead to the installation of excessive storage, because a relatively high hit-ratio can be achieved with small amounts of storage so streaming requirements dominate.

Abstract. Large-scale Video-on-Demand (VoD) systems with high storage and high bandwidth requirements need a substantial amount of resources to store, distribute and transport all of the content and deliver it to the clients. We define an extension to the VoD equipment allocation problem as determining the number and model of VoD servers to install at each potential replica location to minimize deployment costs for a given set of distributed demand and available VoD server models. We propose three novel heuristics that generate near-optimal solutions and show that the number of replica sites for networks where the load is unevenly distributed is low (35 − 45% of potential locations), but that the hit ratios at deployed replicas are high (> 85%).