CAREER: Regulation of Packet-Switched Networks: Stochastic Optimal Control Models and Computational Methods

职业：分组交换网络的调节：随机最优控制模型和计算方法

• 批准号: 9875688
• 负责人: Stephen Patek
• 金额: $ 20万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-06-01 至 2004-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9875688&HistoricalAwards=false
• 关键词: CAREER; Regulation; Packet; Switched; Networks

9875688PatekThe networking research community has recently become intrigued with the possibility of offer-ing differentiated service types which are dynamically priced to enc~6rage favorable consumption of network resources. It is widely speculated but far from clear that pricing can serve as a means of congestion control (packet regulation) as well as a means of cost recovery for infrastructure providers. This work will explore the former assertion. Current mechanisms for the control of packet flows, while often justified by hard engineering anal-ysis, are ad hoc in the sense that they are not conceived as solutions to stochastic optimal control problems. Learning is based mechanisms may be useful in coping with and differentiated services. The research objectives of this proposal are twofold:1. to develop technological mechanisms for packet regulation based on stochastic optimal control and to establish the general applicability of this perspective in networking through mathe-matical and computational analysis, and2. to mitigate the effect of computational complexity in solving real-world stochastic optimal control problems and to promote the development of practical learning based computational methods.The education objectives of this proposal are1. to provide a vehicle by which undergraduate and graduate students will learngeneral approaches to research: modeling, optimization, analysis, and design; andspecific technological expertise: stochastic optimal control, computational methods, andthe cutting edge of data communications, and2. to disseminate the results of the proposed research to as broad an audience as possible, including the telecommunications industry.***

最近，网络研究界对提供不同服务类型的可能性很感兴趣，这些服务类型是动态定价的，以促进网络资源的有利消费。人们普遍猜测，但远不清楚的是，定价可以作为一种拥塞控制手段（分组监管）以及基础设施提供商的成本回收手段。 本书将探讨前一种说法。目前的数据包流控制机制，虽然经常证明了硬工程分析，是特设的意义上说，他们没有被视为随机最优控制问题的解决方案。 基于学习的机制可能有助于应对和差异化服务。本研究的目的有两个：1。开发基于随机最优控制的数据包调节技术机制，并通过数学和计算分析建立这种观点在网络中的普遍适用性; 2. 以减轻计算复杂性对解决实际随机最优控制问题的影响，并促进基于实际学习的计算方法的发展。提供一个工具，本科生和研究生将学习一般的研究方法：建模，优化，分析和设计;和具体的技术专长：随机最优控制，计算方法，和数据通信的前沿，和2。 向尽可能广泛的受众，包括电信业传播拟议研究的结果。