Collaborative Research: Towards An Analytic Foundation for Network Architectures

在大型复杂的通信网络中，关于功能分配的架构决策是极其重要的。为网络架构建立科学基础的时机已经成熟，既可以利用独特的全新设计机会（如GENI和MANET），又可以指导从现有网络架构向新架构的演进。 这样的基础可以导致高效，健壮和可扩展的协议，可能会对通信行业产生重大影响。最近的成功理解协议作为优化器和分层的数学分解提供了一个有前途的起点，这样的分析基础是一个概念上统一的，数学上严格的，和实际相关的。然而，在开发网络架构的分析基础方面仍有许多工作要做。本研究主要集中在三个方面：替代架构的选择：过去的数学结果集中在一个架构来自一个特定的分解。事实上，有很多可供选择的分解方法，它们会导致不同的可伸缩性、收敛性和复杂性权衡。本研究系统地探讨架构选择使用适当的decomposs.Stochastic网络动态：本研究开发新的架构设计考虑到随机（而不是确定性）的网络动态，这是至关重要的建模真实的系统和开发高性能的网络架构。非凸性和鲁棒性：非凸性在真实的网络中持续存在，这可能导致不稳定、性能差和不切实际的计算复杂度。尽管如此，大多数过去的结果已经得出的凸的情况下。本研究探讨了对非凸性具有鲁棒性的架构选择。

In large and complex communication networks, architectural decisions regarding functionality allocation are extremely important. The time is ripe for building a scientific foundation for network architectures, both to capitalize on unique clean-slate design opportunities (such as GENI and MANET) and to guide the evolution from existing network architectures to new ones. Such a foundation can lead to highly efficient, robust, and scalable protocols that could have a significant impact on the communications industry.The recent successes of understanding protocols as optimizers and layering as mathematical decompositions offer a promising starting point for such an analytic foundation one that is conceptually unifying, mathematically rigorous, and practically relevant. However, there is still much work to be done in developing an analytic foundation for network architectures. This research focuses on three main thrusts: Alternative architectural choices: Past mathematical results have focused on one architecture derived from a particular decomposition. There is in fact a wide range of alternative decompositions that result in different scalability, convergence, and complexity tradeoffs. This research systematically explores architectural choices using appropriate decompositions.Stochastic network dynamics: This research develops new architectural designs taking into account stochastic (rather than deterministic) network dynamics, which are critical in modeling real systems and in developing high-performance network architectures. Non-convexity and robustness: Non-convexity persists in real networks, which could lead to instability, poor performance, and impractical computational complexity. Nonetheless, most past results have been derived only for the convex case. This research explores architectural choices that are robust to non-convexity.