Collaborative Research: Towards An Analytic Foundation for Network Architectures

在大型和复杂的通信网络中，有关功能分配的体系结构决策极其重要。为网络体系结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)，也可以引导从现有网络体系结构向新网络体系结构的演变。这样的基础可以导致高效、健壮和可扩展的协议，这可能会对通信行业产生重大影响。最近将协议理解为优化器和将协议分层理解为数学分解的成功为这种分析基础提供了一个有希望的起点，这种分析基础在概念上是统一的，在数学上是严格的，并且在实践上是相关的。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。这项研究集中在三个主要方面：可选的体系结构选择：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。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、收敛和复杂性权衡。本研究使用适当的分解系统地探索体系结构选择。随机网络动力学：本研究开发考虑随机(而不是确定性)网络动力学的新体系结构设计，这在对真实系统建模和开发高性能网络体系结构中至关重要。非凸性和健壮性：非凸性在实际网络中持续存在，这可能会导致不稳定、性能低下和不切实际的计算复杂性。尽管如此，过去的大多数结果都是针对凸集的情况而得出的。这项研究探索了对非凸性具有健壮性的建筑选择。

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.