基于队列大小反馈的NDN拥塞有效控制

Sichen Song, Lixia Zhang
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引用次数: 4

摘要

命名数据网络(NDN)可以通过其内置的多播数据传递、网络内缓存和支持多路径转发的能力来提高用户数据检索吞吐量。然而,它们的实现也带来了挑战。在这项工作中,我们首先研究了多路径转发和网络内缓存如何干扰用于拥塞控制的消费者测量。基于这些结果,我们提出了一个拥塞控制解决方案,NDN-QSF,它可以在网络内缓存的存在下有效地工作。在NDN-QSF中,转发器估计上游带宽并使用队列大小作为拥塞反馈通知下游路由器限制兴趣传输速率。我们进一步调整和扩展了NDN-QSF,使路由器能够做出明智的多路径转发决策。通过仿真实验对NDN-QSF进行了评估,结果表明,NDN-QSF可以有效地利用队列大小作为拥塞反馈来控制拥塞,并通过多路径转发提高网络吞吐量。
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Effective NDN congestion control based on queue size feedback
Named data networking (NDN) can improve the consumer data retrieval throughput with its built-in multicast data delivery, innetwork caching, and ability to support multi-path forwarding. However, their realization brings challenges. In this work, we first examine how multi-path forwarding and in-network caching can interfere with consumer measurements for congestion control. Based on the results, we propose a congestion control solution, NDN-QSF, that can work effectively in the presence of in-network caching. In NDN-QSF, forwarders estimate upstream bandwidth and use queue size as congestion feedback to inform downstream routers to limit interest transmission rates. We further adapt and extend NDN-QSF to enable routers to make informed multi-path forwarding decisions. We evaluated NDN-QSF through simulation experimentation and our results show that NDN-QSF can effectively control congestion by using queue size as congestion feedback and improve network throughput with multi-path forwarding.
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