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Server Selection in Large-Scale Video-on-Demand Systems

机译:大型视频点播系统中的服务器选择

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Video on demand, particularly with user-generated content, is emerging as one of the most bandwidth-intensive applications on the Internet. Owing to content control and other issues, some video-on-demand systems attempt to prevent downloading and peer-to-peer content delivery. Instead, such systems rely on server replication, such as via third-party content distribution networks, to support video streaming (or pseudostreaming) to their clients. A major issue with such systems is the cost of the required server resources.rnBy synchronizing the video streams for clients that make closely spaced requests for the same video from the same server, server costs (such as for retrieval of the video data from disk) can be amortized over multiple requests. A fundamental trade-off then arises, however, with respect to server selection. Network delivery cost is minimized by selecting the nearest server, while server cost is minimized by directing closely spaced requests for the same video to a common server.rnThis article compares classes of server selection policies within the context of a simple system model. We conclude that: (ⅰ) server selection using dynamic system state information (rather than only proximities and average loads) can yield large improvements in performance, (ⅱ) deferring server selection for a request as late as possible (i.e., until just before streaming is to begin) can yield additional large improvements, and (ⅲ) within the class of policies using dynamic state information and deferred selection, policies using only "local" (rather than global) request information are able to achieve most of the potential performance gains.
机译:视频点播,特别是带有用户生成内容的视频,正在成为Internet上带宽最密集的应用程序之一。由于内容控制和其他问题,一些视频点播系统试图阻止下载和对等内容交付。相反,此类系统依赖于服务器复制(例如,通过第三方内容分发网络)来支持向其客户端的视频流(或伪流)。此类系统的主要问题是所需服务器资源的成本。rn通过同步对来自同一服务器的相同视频的请求间隔很小的客户端的视频流,服务器成本(例如从磁盘检索视频数据)可以在多个请求中摊销。然而,在服务器选择方面出现了基本的权衡。通过选择最近的服务器,可以将网络交付成本降到最低,而通过将对同一视频的近距离请求定向到公共服务器,可以将服务器成本降到最低。本文在简单系统模型的上下文中比较了服务器选择策略的类别。我们得出以下结论:(ⅰ)使用动态系统状态信息(而不只是邻近和平均负载)进行服务器选择可以显着提高性能;(ⅱ)尽可能延迟请求的服务器选择(即直到流传输之前) (即将开始)可以带来更大的改进,并且(ⅲ)在使用动态状态信息和延迟选择的策略类别内,仅使用“本地”(而不是全局)请求信息的策略可以实现大部分潜在的性能提升。

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