一个数据包就足够了--有限存储的高效分组化网络编码
One Packet Suffices - Highly Efficient Packetized Network Coding With Finite Memory
摘要：
随机线性网络编码（RLNC）已经成为一个强大的工具，鲁棒的高吞吐量组播。投影分析-最近推出的技术-表明，分布式分组化RLNC协议实现（顺序）最佳和完美的流水线信息传播在许多设置。在RNLC的原始方法中，中间结点将所有可用信息编码在一起。这需要中间结点保持相当多的数据可用于编码。此外，它导致编码复杂度随着该数据的大小线性增长。虽然这已经被识别为一个问题，但组合排队理论和网络编码的方法迄今为止还没有提供中间节点处的网络编码的存储器需求的简洁表示。本文显示了令人惊讶的结果，在所有设置与连续的数据流，网络编码继续执行最佳，即使每个节点只有一个数据包被保存在活动内存中，并用于计算。这导致了一个非常简单的RLNC协议变体，大大降低了对计算和内存资源的要求。通过扩展的投影分析，我们表明，在所有的设置中，RLNC协议被证明是最佳的有限内存的变体同样表现良好。与原始投影分析相同，我们的技术适用于各种网络模型，包括高度动态的拓扑结构，这些拓扑结构可以随时以对抗的方式完全改变。
英文摘要：
Random Linear Network Coding (RLNC) has emerged as a powerful tool for robust high-throughput multicast. Projection analysis - a recently introduced technique - shows that the distributed packetized RLNC protocol achieves (order) optimal and perfectly pipelined information dissemination in many settings. In the original approach to RNLC intermediate nodes code together all available information. This requires intermediate nodes to keep considerable data available for coding. Moreover, it results in a coding complexity that grows linearly with the size of this data. While this has been identified as a problem, approaches that combine queuing theory and network coding have heretofore not provided a succinct representation of the memory needs of network coding at intermediates nodes. This paper shows the surprising result that, in all settings with a continuous stream of data, network coding continues to perform optimally even if only one packet per node is kept in active memory and used for computations. This leads to an extremely simple RLNC protocol variant with drastically reduced requirements on computational and memory resources. By extending the projection analysis, we show that in all settings in which the RLNC protocol was proven to be optimal its finite memory variant performs equally well. In the same way as the original projection analysis, our technique applies in a wide variety of network models, including highly dynamic topologies that can change completely at any time in an adversarial fashion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
