Managing Large-Scale Computer Communication Networks Using Adaptive Learning Systems

9908578通过分布式代理管理大型计算机通信网络是自动化和可扩展网络管理的一个新兴研究领域。在本研究的背景下，agent是一种简单的算法，它可以驻留在网络节点上，并使用本地信息对网络的状态进行本地决策。由于许多这样的代理可以在大型网络中以分布式方式使用，因此很少有人知道本地决策如何导致网络级别的集体效应，以及网络节点上的代理可以使用哪些分散算法，以便在网络范围内实现良好的全局性能。这些问题的答案对于下一代网络从以设备为中心向以网络为中心推进网络管理至关重要。这个项目的目标是研究适应性学习方法作为这些开放问题的潜在解决方案。特别是，动态概率图模型将被研究和展示，以提供完成以下任务的系统方法：(1)开发（局部）模型来合作和聚合信息，形成代理的邻域；(2)使用（局部）模型来自适应地在网络节点上执行分布式决策；(3)通过聚合局部统计来开发（全局）模型，以表征代理网络的全局效应。在自适应学习系统领域的一套丰富的方法，特别是动态图模型，将显示为管理具有不规则和变化的拓扑结构和不可访问的网络组件的大型网络提供理论基础和实践方法

9908578JiManaging large computer communication networks through distributed agents is an emerg-ing area of research towards automated and scaleable network management. In the context of this research, an agent is a simple algorithm which can reside at a network node, and perform local decisions on the state of a network using local information. As many such agents can be used in a distributed fashion at a large network, little is known how local decisions result in collective effects at network level, and what decentralized algorithms can be used by agents at network nodes so that a good global performance can be achieved network-wide. To provide answers to these questions is crucial to advancing network management from device-centric to network-centric which is required by the next generation networks.The goal of this project is to investigate adaptive learning approaches as potential solutions to these open problems. In particular, dynamic probabilistic graph models will be investigated and shown to provide a systematic approach for accomplishing the following tasks:(1) developing (local) models to cooperate and aggregate information form a neighborhood of agents,(2) using the (local) models developed to perform distributed decisions at network nodes adaptively,(3) developing (global) model by aggregating local statistics to characterize resulting global effects of a network of agents.A rich set of methods in the area of adaptive learning systems, especially the dynamic graph models, will be shown to provide both a theoretical foundation, and practical approaches in managing large networks with an irregular and a changing topology, and inaccessible network components.***