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On achieving maximum multicast throughput in undirected networks

机译：在无向网络中实现最大组播吞吐量

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摘要

The transmission of information within a data network is constrained by the network topology and link capacities. In this paper, we study the fundamental upper bound of information dissemination rates with these constraints in undirected networks, given the unique replicable and encodable properties of information flows. Based on recent advances in network coding and classical modeling techniques in flow networks, we provide a natural linear programming formulation of the maximum multicast rate problem. By applying Lagrangian relaxation on the primal and the dual linear programs (LPs), respectively, we derive a) a necessary and sufficient condition characterizing multicast rate feasibility, and b) an efficient and distributed subgradient algorithm for computing the maximum multicast rate. We also extend our discussions to multiple communication sessions, as well as to overlay and ad hoc network models. Both our theoretical and simulation results conclude that, network coding may not be instrumental to achieve better maximum multicast rates in most cases; rather, it facilitates the design of significantly more efficient algorithms to achieve such optimality.
机译：数据网络内信息的传输受到网络拓扑和链接容量的限制。在本文中，鉴于信息流具有独特的可复制和可编码特性，我们研究了在无向网络中具有这些约束的信息传播速率的基本上限。基于流网络中网络编码和经典建模技术的最新进展，我们提供了最大组播速率问题的自然线性规划公式。通过分别对原始和对偶线性程序（LP）应用拉格朗日松弛，我们得出a）表征组播速率可行性的必要条件和充分条件，以及b）计算最大组播速率的高效且分布式次梯度算法。我们还将讨论扩展到多个通信会话，以及覆盖网络和临时网络模型。我们的理论和仿真结果都得出结论，在大多数情况下，网络编码可能无法达到更好的最大组播速率。相反，它有助于设计出效率更高的算法来实现这种最优性。

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