CAREER: Scalable Architectures for Self-Managed Networks
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.
Principal Investigator: Murat Alanyali
Sponsor: National Science Foundation