CAREER: Scalable Architectures for Self-Managed Networks

提出的方案涉及基于个人用户强化学习的分布式网络架构。该项目的主要目标是开发一种适用于大型网络的动态和分布式资源共享机制的通用框架。考虑一种通用公式，其中用户以几种可选的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，而这个结果还取决于其他用户的行为。所提出的框架涉及网络用户基于这些局部信息进行非合作决策，反过来它在网络规模上是可扩展的。该研究计划将根据算法参数渐近精确的非线性微分方程确定网络的宏观动力学。极限系统与进化生物学中出现的某些动力系统密切相关。成功完成的项目将确定可能的平衡机制，将描述导致稳定网络运行的分布式算法，并将开发网络设计和管理指南，以维持网络的理想运行机制。所提出的算法的鲁棒性将进行分析研究，并通过模拟和实验验证所获得的结果。拟议项目的教育方面包括课程开发和教学，最终目标是建立一个强大的研究项目和密切的工业合作，研究生和本科生水平的指导，以及本科生积极参与研究。

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.