CAREER: Scalable Architectures for Self-Managed Networks

所提出的计划涉及分布式网络架构，基于个人用户的强化学习。 该计划的主要目标是开发一个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。 一个通用的配方被认为是用户访问网络资源的几种替代方式之一。 每个用户仅限于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。 所提出的框架涉及非合作的决策网络用户的基础上，这种本地信息，反过来，它是可扩展的网络的大小。 该研究计划将确定宏观动态的网络的非线性微分方程是渐近精确的算法参数。 极限系统与进化生物学中出现的某些动力系统密切相关。 成功完成的计划将确定可能的平衡制度，将表征分布式算法，导致稳定的网络运行，并将制定网络设计和管理准则，保持所需的网络运行制度。 将通过分析研究所提出的算法的鲁棒性，并通过模拟和实验验证所获得的结果。所提出的计划的教育方面涉及课程开发和教学，最终目标是强大的研究计划和密切的工业合作，在研究生和本科生两个层次的指导，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.