In this paper, we explore the convergence of the caching and streaming technologies for Internet multimedia. The paper describes a design for a streaming and caching architecture to be deployed on broadband networks. The basis of the work is the proposed Internet standard, Real Time Streaming Protocol (RTSP), likely to be the de-facto standard for web-based A/V caching and streaming, in the near future. The proxies are all managed by an `Intelligent Agent' or `Broker' - this has been designed as an enhanced RTSP proxy server that maintains the state information that is so essential in streaming of media data. In addition, all the caching algorithms run on the broker. Having an intelligent agent or broker ensures that the `simple' caching servers can be easily embedded into the network. However, RTSP does nor have the right model for doing broker based streaming/caching architecture. The work reported here is an attempt to contribute towards that end.
在本文中,我们探讨了Internet多媒体的缓存 I>和流媒体 I>技术的融合。本文描述了一种将在宽带网络上部署的流和缓存体系结构的设计。这项工作的基础是提议的Internet标准实时流协议(RTSP),在不久的将来可能成为基于Web的A / V缓存和流的 facto I>标准。代理服务器全部由“智能代理”或“代理”管理-它被设计为增强了RTSP代理服务器的I,用于维护媒体数据流中必不可少的状态信息。此外,所有缓存算法都在代理上运行。拥有智能代理或代理可确保“简单”缓存服务器可以轻松地嵌入到网络中。但是,RTSP也不具有执行基于代理的流/缓存体系结构的正确模型。这里报道的工作是为此目的做出的努力。 P>
机译:具有流处理框架的列访问感知的流内数据缓存
机译:通过结合使用拆分缓存,受害者缓存和流缓冲区来提高数据缓存性能
机译:用于半流数据处理的基于缓存的流关系联接运算符
机译:大都市中延迟高效的视频流:缓存框架
机译:Domical:无线家庭网络中用于流媒体的新的协作式缓存框架。
机译:中脑多巴胺神经元在一个通用框架中计算推断和缓存的值预测误差
机译:缓存友好的自适应视频流框架利用内容中心网络中的正则表达式