FIND: Collaborative Research: Towards An Analytic Foundation for Network Architectures

在大型和复杂的通信网络中，关于功能分配的体系结构决策通常比资源分配算法本身的细节更重要。这个由美国国家科学基金会资助的项目旨在通过将协议理解为优化器和将分层理解为数学分解的最新成功，为设计网络体系结构奠定科学基础。特别是，五个机构的私人投资机构合作开展了广泛的密切联系的研究活动，大大提高了最先进的水平。从架构设计问题的凸优化公式出发，该项目研究了提供不同可伸缩性、收敛和复杂性权衡的各种可选分解。然后，PI确定这些可选架构的属性在随机网络动态和非凸目标和约束下是否继续保持，并通过对这种动态的仔细研究来开发新的架构设计。在数学上，该项目导致了网络优化和随机网络理论之间期待已久的结合，并使系统方法能够利用一般非凸优化方面的进展。广泛影响：该项目与NSF的GENI倡议具有明显的协同效应。这项研究为未来网络体系结构的设计提供了强有力的分析基础，包括与当今互联网背道而驰的全新解决方案。考虑到网络动态和非凸性的影响，探索分解功能的新方法将导致可以在GENI基础设施中评估的新协议和机制。

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.