流量散度理论：一种用于动态网络的分析形式体系
Traffic Divergence Theory: An Analysis Formalism for Dynamic Networks
AI总结：
本文提出基于流量散度的动态网络分析理论，用空间散度率刻画最大流量分布，并通过数据中心吞吐量估计与机器人通信规划仿真验证其有效性。
摘要：
流量动态在分析和设计几乎任何网络时都至关重要。本文提出了一种分析网络流量动态的新颖理论方法。该理论的机制基于流量散度概念，用于刻画网络节点和链路的流量平衡与不平衡状态。它提供了多种分析探针，以研究空间和时间两方面的流量动态。特别是，网络中的最大流量分布可由空间流量散度率表征，该指标揭示了节点流量散度之间的相对差异。为说明其实用性，我们将该理论应用于两个由网络驱动的问题：数据中心网络的吞吐量估计，以及机器人网络的功率优化通信规划，并通过仿真展示了所提理论的优势。
英文摘要：
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.
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。
