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TLed: Time-Lived Based Congestion and Rate Control for Video in Named Data Networking

机译:TLed:命名数据网络中基于时间的视频拥塞和速率控制

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Named Data Networking (NDN), a revolution of the IP architecture, provides an information-centric paradigm to transmit video traffic which occupies most of the Internet traffic. However, the control schemes in the field of video NDN research can easily result in full bottleneck bandwidth at the cost of high video delay and frequent packet loss. This is because the loss-based or transient-queue-delay-based congestion control schemes respond to the signal after the congestion is formed. And NDN multi-source feature can't make the loss and delay-based signals accurately reflect the network situation. In addition, the current video applications need to choose the suitable video rate to stream across the network. The bandwidth estimation scheme of video rate control ignoring multi-source reduces accuracy in NDN. In this paper, we firstly build a max-plus algebra based model to characterize the interest-data transmission process for video consumer and to estimate the NDN bandwidth delay product. The hops (lived time)that data pass through from the producer to the consumer represent the multi-source feature in the model. Then we propose a Time-Lived based congestion and rate control scheme for the NDN video consumer. Extensive experiments have been conducted to evaluate our scheme from the perspective of throughput, delay, and queue length. The results show that our scheme can improve the throughput of the video consumer while reducing the round trip time by shortening the queue length.
机译:命名数据网络(NDN)是IP体系结构的革命,它提供了一种以信息为中心的范例来传输视频流量,该视频流量占用了大多数Internet流量。但是,视频NDN研究领域中的控制方案很容易导致完整的瓶颈带宽,但代价是视频延迟高且数据包丢失频繁。这是因为基于损耗或基于瞬态队列延迟的拥塞控制方案会在拥塞形成后对信号做出响应。 NDN多源功能无法使基于损耗和延迟的信号准确反映网络状况。此外,当前的视频应用程序需要选择合适的视频速率以在网络上传输。忽略多源的视频速率控制的带宽估计方案降低了NDN的准确性。在本文中,我们首先建立了一个基于max-plus代数的模型,以表征视频消费者的兴趣数据传输过程,并估计NDN带宽延迟积。数据从生产者传递到消费者的跃点(生存时间)代表了模型中的多源功能。然后,我们为NDN视频用户提出了一种基于时间的拥塞和速率控制方案。已经从吞吐量,延迟和队列长度的角度进行了广泛的实验,以评估我们的方案。结果表明,我们的方案可以通过缩短队列长度来提高视频用户的吞吐量,同时减少往返时间。

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