ABSTRACT

In large and complex communication networks, architectural decisions regarding functionality allocation are often more important than the details of resource allocation algorithms themselves. This NSF-funded project aims to develop a scientific foundation for designing network architectures by building upon recent successes in understanding protocols as optimizers and layering as mathematical decompositions. In particular, the PIs at five institutions collaborate to conduct a wide range of closely-connected research activities that substantially improve upon the state-of-the-art. Starting from a convex optimization formulation of the architecture design problem, the project investigates a wide range of alternative decompositions that provide different scalability, convergence, and complexity tradeoffs. The PIs then determine whether the properties of these alternative architectures continue to hold under stochastic network dynamics and non-convex objectives and constraints, and develop new architectural designs from a careful study of such dynamics. Mathematically, this project leads to a long-overdue union between network optimization and stochastic networks theory, and enables a systematic approach to leverage advances in general non-convex optimization.

Broader Impact: This project has clear synergy with the NSF's GENI initiative. The research provides a strong, analytic foundation for the design of future network architectures, including clean-slate solutions that deviate from todays Internet. The exploration of new ways to decompose functionality, with the influence of network dynamics and non-convexity in mind, will result in new protocols and mechanisms that can be evaluated in the GENI infrastructure.

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