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非常重要。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），也可以指导从现有网络架构向新网络架构的演变。这样的基础可以产生高效、健壮和可扩展的协议，从而对通信行业产生重大影响。最近成功地将协议理解为优化器，将分层理解为数学分解，这为这种分析基础提供了一个有希望的起点，这种基础在概念上是统一的，在数学上是严格的，并且与实际相关。然而，在开发网络体系结构的分析基础方面仍有许多工作要做。这项研究主要集中在三个方面：可选择的体系结构选择：过去的数学结果集中在从特定分解派生的一个体系结构上。实际上，存在各种各样的可选分解，这些分解会导致不同的可伸缩性、收敛性和复杂性权衡。本研究使用适当的分解系统地探索了架构选择。随机网络动力学：本研究开发了考虑随机（而非确定性）网络动力学的新架构设计，这对于建模真实系统和开发高性能网络架构至关重要。非凸性和鲁棒性：非凸性在现实网络中持续存在，这可能导致不稳定、性能差和不切实际的计算复杂性。尽管如此，大多数过去的结果都是针对凸情况得出的。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.