按需IPTV业务的容量需求

Pat Diminico, V. Gopalakrishnan, R. Jana, K. Ramakrishnan, Deborah F. Swayne, V. Vaishampayan
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引用次数: 4

摘要

服务提供商正在不断发展,以提供更多的视频点播内容。顾客喜欢在方便的时候观看自己选择的各种娱乐内容。为不断增长的按需观看者提供服务,需要提供商仔细配置,以适应规模和交互性。在本文中,我们研究了全国IPTV服务的数十万消费者的长期使用模式,并确认观众确实正在转向使用数字录像机或点播观看所谓的“时移”电视节目和电影。我们还检查了这些用户使用“流控制”功能(例如,快进,倒带,跳过,重放等)对观看体验的交互式控制的影响。通过在IPTV服务器上的仔细测量,我们计算了视频流的负载,并处理了这些流控制事件。然后,我们从这些微基准测量中推断,以预测如果提供这样的服务,用户将使用“基于网络的”DVR功能所施加的处理负载。我们使用详细的跟踪驱动模拟和简单的基于操作分析的模型来预测视频中心办公室中服务器综合体的容量需求,以服务大量客户(例如,像孟买这样人口稠密的城市)。我们提供了关于服务器服务的请求数量、服务这些请求的平均时间以及客户端感知到的响应时间的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Capacity requirements for on-demand IPTV services
Service providers are evolving to provide more video content on-demand. Customers like to watch a variety of entertainment content of their choice and at their convenience. Serving this ever-increasing base of on-demand viewers requires careful provisioning by the providers to accommodate for both scale and interactivity. In this paper, we examine long-term usage patterns of hundreds of thousands of consumers of a nationwide IPTV service, and confirm that viewers are indeed migrating to what is called “time-shifted” viewing of television programming and movies using digital video recorders or on-demand viewing. We also examine the impact of such user's interactive control of their viewing experience using “stream control” functions (e.g., fast-forward, rewind, skip, replay, etc.) Through careful measurements on an IPTV server, we compute the load due to video streaming and handling these stream control events. We then extrapolate from these micro-benchmark measurements to predict the processing load imposed by users that would resort to using a “network-based” DVR capability if such a service were offered. We use both detailed trace-driven simulations and a simple operational-analysis based model to predict the capacity requirements of the server complex in a video-hub office to serve a large population of customers (e.g., a densely populated city like Mumbai). We provide insights on the number of requests serviced by the server, the average time to service these requests and the response time as perceived by the client.
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