Traffic dynamics is universally crucial in analyzing and designing almost any network. This article introduces a novel theoretical approach to analyzing network traffic dynamics. This theory's machinery is based on the notion of traffic divergence, which captures the flow (im)balance of network nodes and links. It features various analytical probes to investigate both spatial and temporal traffic dynamics. In particular, the maximal traffic distribution in a network can be characterized by spatial traffic divergence rate, which reveals the relative difference among node traffic divergence. To illustrate the usefulness, we apply the theory to two network-driven problems: throughput estimation of data center networks and power-optimized communication planning for robot networks, and show the merits of the proposed theory through simulations.

翻译：流量动态性在几乎所有网络的分析与设计中普遍具有关键作用。本文提出了一种分析网络流量动态性的全新理论方法。该理论的核心机制基于流量散度概念，用于刻画网络节点与链路的流（非）平衡状态。该理论具备多种分析探针，可分别研究空间与时间维度的流量动态性。特别地，网络中的最大流量分布可通过空间流量散度率进行刻画，该参数揭示了节点流量散度之间的相对差异。为说明其应用价值，我们将该理论应用于两个网络驱动问题：数据中心网络的吞吐量估计与机器人网络的功率优化通信规划，并通过仿真验证了所提理论的优越性。