CAREER: Scalable Architectures for Self-Managed Networks

职业：自我管理网络的可扩展架构

• 批准号: 0238397
• 负责人: Murat Alanyali
• 金额: $ 40万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0238397&HistoricalAwards=false
• 关键词: CAREER; Scalable; Architectures; Self; Managed

The proposed program concerns distributed network architectures that are based on reinforcement learning by individual users. The main goal of the program is to develop a general framework for dynamic and distributed resource sharing mechanisms that are suitable for large networks. A generic formulation is considered in which users access network resources in one of several alternative ways. Each user is restricted to observe its own interpretation of the outcomes only, where this outcome depends also on the actions of other users. The proposed framework involves non-cooperative decision making by network users based on this local information, in turn it is scalable in the size of the network. The research program will identify macroscopic dynamics of the network in terms of nonlinear differential equations that are asymptotically exact in algorithmic parameters. The limit system is closely related to certain dynamical systems that arise in the context of evolutionary biology. Successfully completed program will identify possible equilibrium regimes, will characterize distributed algorithms that lead to stable network operation, and will develop network design and management guidelines that maintain desired operating regimes for the network. Robustness of the proposed algorithms will be investigated analytically, and verification of obtained results will be carried out via simulations and experiments.Educational aspects of the proposed program involves course development and teaching with the ultimate goal of a strong research program and close industrial collaboration, mentoring at both graduate and undergraduate levels, and active research participation from undergraduate students.

所提出的程序涉及基于个人用户强化学习的分布式网络架构。 该计划的主要目标是开发适用于大型网络的动态分布式资源共享机制的通用框架。 考虑一种通用的表述，其中用户以几种替代方式之一访问网络资源。 每个用户只能观察自己对结果的解释，其中该结果还取决于其他用户的操作。 所提出的框架涉及网络用户基于本地信息做出的非合作决策，反过来，它的网络规模是可扩展的。 该研究计划将根据算法参数渐近精确的非线性微分方程来识别网络的宏观动力学。 极限系统与进化生物学背景下出现的某些动力系统密切相关。 成功完成的计划将确定可能的平衡状态，描述导致稳定网络运行的分布式算法的特征，并将制定网络设计和管理指南以维持网络所需的运行状态。 将对所提出的算法的鲁棒性进行分析研究，并通过模拟和实验对所获得的结果进行验证。所提出的计划的教育方面涉及课程开发和教学，其最终目标是强大的研究计划和密切的工业合作、指导研究生和本科生的水平，以及本科生的积极研究参与。