Building Internet caching systems for streaming media delivery

The proxy has been widely and successfully used to cache the static Web objects fetched by a client so that the subsequent clients requesting the same Web objects can be served directly from the proxy instead of other sources faraway, thus reducing the server\u27s load, the network traffic and the client response time. However, with the dramatic increase of streaming media objects emerging on the Internet, the existing proxy cannot efficiently deliver them due to their large sizes and client real time requirements.;In this dissertation, we design, implement, and evaluate cost-effective and high performance proxy-based Internet caching systems for streaming media delivery. Addressing the conflicting performance objectives for streaming media delivery, we first propose an efficient segment-based streaming media proxy system model. This model has guided us to design a practical streaming proxy, called Hyper-Proxy, aiming at delivering the streaming media data to clients with minimum playback jitter and a small startup latency, while achieving high caching performance. Second, we have implemented Hyper-Proxy by leveraging the existing Internet infrastructure. Hyper-Proxy enables the streaming service on the common Web servers. The evaluation of Hyper-Proxy on the global Internet environment and the local network environment shows it can provide satisfying streaming performance to clients while maintaining a good cache performance. Finally, to further improve the streaming delivery efficiency, we propose a group of the Shared Running Buffers (SRB) based proxy caching techniques to effectively utilize proxy\u27s memory. SRB algorithms can significantly reduce the media server/proxy\u27s load and network traffic and relieve the bottlenecks of the disk bandwidth and the network bandwidth.;The contributions of this dissertation are threefold: (1) we have studied several critical performance trade-offs and provided insights into Internet media content caching and delivery. Our understanding further leads us to establish an effective streaming system optimization model; (2) we have designed and evaluated several efficient algorithms to support Internet streaming content delivery, including segment caching, segment prefetching, and memory locality exploitation for streaming; (3) having addressed several system challenges, we have successfully implemented a real streaming proxy system and deployed it in a large industrial enterprise

Video-on-Demand over Internet: a survey of existing systems and solutions

Video-on-Demand is a service where movies are delivered to distributed users with low delay and free interactivity. The traditional client/server architecture experiences scalability issues to provide video streaming services, so there have been many proposals of systems, mostly based on a peer-to-peer or on a hybrid server/peer-to-peer solution, to solve this issue. This work presents a survey of the currently existing or proposed systems and solutions, based upon a subset of representative systems, and defines selection criteria allowing to classify these systems. These criteria are based on common questions such as, for example, is it video-on-demand or live streaming, is the architecture based on content delivery network, peer-to-peer or both, is the delivery overlay tree-based or mesh-based, is the system push-based or pull-based, single-stream or multi-streams, does it use data coding, and how do the clients choose their peers. Representative systems are briefly described to give a summarized overview of the proposed solutions, and four ones are analyzed in details. Finally, it is attempted to evaluate the most promising solutions for future experiments. Résumé La vidéo à la demande est un service où des films sont fournis à distance aux utilisateurs avec u

Modelling contents status for IPTV delivery networks

Since IPTV has been invented, IPTV is considered a dominant technology to distribute high quality videos and live channels anytime anywhere over challenging environment to end users who are having different preferences and demands. Presently, IPTV service providers manage IPTV delivery networks, in terms of contents, channels, resources, based on contents popularity distribution and/or users’ preferences only. Although content popularity and users’ preferences play an important role to cope with the increasing demand of IPTV contents/channels, these two measures fail in producing efficient IPTV delivery networks For that, IPTV delivery network designing should integrate the IPTV content characteristics like size, interactivity, the rapid changing lifetime. Therefore, the idea of this paper is to build a mathematical model that integrates all these factors in one concept called IPTV content status. Modeling the contents status according to its characteristics is an important point to design Content-Aware IPTV delivery networks.The experimental results showed the superiority of modeling IPTV content status in balancing the load and reducing the resources waste

On Applications of Relational Data

With the advances of technology and the popularity of the Internet, a large amount of data is being generated and collected. Much of these data is relational data, which describe how people and things, or entities, are related to one another. For example, data from sale transactions on e-commerce websites tell us which customers buy or view which products. Analyzing the known relationships from relational data can help us to discover knowledge that can benefit businesses, organizations, and our lives. For instance, learning the products that are commonly bought together allows businesses to recommend products to customers and increase their sales. Hidden or new relationships can also be inferred based on relational data. In addition, based on the connections among the entities, we can approximate the level of relatedness between two entities, even though their relationship may be hard to observe or quantify. This research aims to explore novel applications of relational data that will help to improve our life in various aspects, such as improving business operations, improving experiences in using online services, and improving health care services. In applying relational data in any domain, there are two common challenges. First, the size of the data can be massive, but many applications require that results are obtained within a short time. Second, relational data are often noisy and incomplete. Many relationships are extracted automatically from text resources, and hence they are prone to errors. Our goal is not only to propose novel applications of relational data but also to develop techniques and algorithms that will facilitate and make such applications practical. This work addresses three novel applications of relational data. The first application is to use relational data to improve user experiences in online video sharing services. Second, we propose the use of relational data to find entities that are closely related to one another. Such problems arise in various domains, such as product recommendation and query suggestion. Third, we propose the use of relational data to assist medical practitioners in drug prescription. For these applications, we introduce several techniques and algorithms to address the aforementioned challenges in using relational data. Our approaches are evaluated extensively to demonstrate their effectiveness. The approaches proposed in this work not only can be used in the specific applications we discuss but also can help to facilitate and promote the use of relational data in other application domains