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Enhancing Scalability in On-Demand Video Streaming Services for P2P Systems

机译:提高P2P系统点播视频流服务中的可扩展性

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摘要

Recently, many video applications like video telephony, video conferencing, Video-on-Demand (VoD), and so forth have produced heterogeneous consumers in the Internet. In such a scenario, media servers play vital role when a large number of concurrent requests are sent by heterogeneous users. Moreover, the server and distributed client systems participating in the Internet communication have to provide suitable resources to heterogeneous users to meet their requirements satisfactorily. The challenges in providing suitable resources are to analyze the user service pattern, bandwidth and buffer availability, nature of applications used, and Quality of Service (QoS) requirements for the heterogeneous users. Therefore, it is necessary to provide suitable techniques to handle these challenges. In this paper, we propose a framework for peer-to-peer- (P2P-) based VoD service in order to provide effective video streaming. It consists of four functional modules, namely, Quality Preserving Multivariate Video Model (QPMVM) for efficient server management, tracker for efficient peer management, heuristic-based content distribution, and light weight incentivized sharing mechanism. The first two of these modules are confined to a single entity of the framework while the other two are distributed across entities. Experimental results show that the proposed framework avoids overloading the server, increases the number of clients served, and does not compromise on QoS, irrespective of the fact that the expected framework is slightly reduced.
机译:最近,许多视频应用,如视频电话,视频会议,视频点播(VOD)等,在互联网中产生了异构的消费者。在这种情况下,当异构用户发送大量的并发请求时,媒体服务器发挥重要作用。此外,参与互联网通信的服务器和分布式客户端系统必须为异质用户提供合适的资源,以满足他们的要求。提供合适资源的挑战是分析用户服务模式,带宽和缓冲区可用性,所使用的应用的性质,以及异构用户的服务质量(QoS)要求。因此,有必要提供处理这些挑战的合适技术。在本文中,我们提出了一种基于对等(P2P-)VOD服务的框架,以提供有效的视频流。它由四个功能模块组成,即高效服务器管理的质量保留多元视频模型(QPMVM),用于高效的对等管理,启发式的内容分发和轻量级激励共享机制的跟踪器。这些模块中的前两个被限制在框架的单个实体上,而另外两个被分布在实体上。实验结果表明,建议的框架避免了服务器过载,增加了所服务的客户数量,并且在QoS上不会妥协,而不管预期框架略微降低。

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