命名数据网络中Wi-Fi媒体流的测量研究

Samiuddin Mohammed, Mengjun Xie
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引用次数: 4

摘要

数据网络(NDN),又称内容中心网络(CCN),擅长内容分发,尤其是多媒体分发,这需要消耗大量的网络带宽。随着移动设备的市场渗透和无线技术的进步,基于Wi-Fi的媒体流越来越受欢迎,但它在当今基于IP的网络中并没有很好地扩展。因此,一个自然的问题是如何利用NDN来改进和优化Wi-Fi上的媒体流。作为解决这一问题的第一步,我们搭建了一个基于Wi-Fi Direct技术的5节点Wi-Fi流媒体试验台,并使用它来采集NDN和IP组网中流媒体时的带宽和CPU使用数据。我们测试了4个流媒体场景,其中一个实时视频通过Wi-Fi直接从一个发布者流到多个消费者,并在本文中展示了我们的测量结果。我们的实验结果表明,通过Wi-Fi的内容发布者和其转发器(即接入点)之间的带宽消耗可以通过NDN有效地显著减少,提供比IP更好的可扩展性。然而,NDN的CPU使用率可能比IP高得多,这值得进一步研究和优化。
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A Measurement Study on Media Streaming over Wi-Fi in Named Data Networking
Named Data Networking (NDN), aka Content Centric Networking (CCN), excels in content distribution especially multimedia distribution, which can consume significant network bandwidth. With the market penetration of mobile devices and advancement of wireless technologies, media streaming over Wi-Fi becomes increasingly popular but it does not scale well in today's IP based networking. A natural question therefore is how to leverage NDN to improve and optimize media streaming over Wi-Fi. As a first step towards this problem, we set up a 5-node Wi-Fi media streaming test bed based on Wi-Fi Direct technology and use it to collect the bandwidth and CPU usage data when streaming media in NDN as well as in IP networking. We test 4 streaming scenarios in which a live video is streamed from one publisher to multiple consumers over Wi-Fi Direct and present our measurement results in this paper. Our experimental results indicate that the bandwidth consumption between a content publisher and its forwarder (i.e., Access point) over Wi-Fi can be effectively and dramatically reduced by NDN, offering much better scalability than IP. However, CPU usage can become much higher in NDN than in IP, which deserves further investigation and optimization.
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