Collaborative Research: Towards An Analytic Foundation for Network Architectures

协作研究：建立网络架构的分析基础

• 批准号: 0634898
• 负责人: Sanjay Shakkottai
• 金额: $ 6.65万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0634898&HistoricalAwards=false
• 关键词: Collaborative; Research; Towards; Analytic; Foundation

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.

在大型且复杂的通信网络中，有关功能分配的架构决策极其重要。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（例如 GENI 和 MANET），也可以指导从现有网络架构向新网络架构的演进。 这样的基础可以产生高效、健壮和可扩展的协议，这些协议可能对通信行业产生重大影响。最近将协议理解为优化器并将分层理解为数学分解的成功为这种概念上统一、数学上严格且实用的分析基础提供了一个有希望的起点。然而，在开发网络架构的分析基础方面仍有许多工作要做。这项研究重点关注三个主要方向： 替代架构选择：过去的数学结果集中于从特定分解派生的一种架构。事实上，存在多种替代分解，它们会导致不同的可扩展性、收敛性和复杂性权衡。这项研究使用适当的分解来系统地探索架构选择。随机网络动态：这项研究开发了新的架构设计，考虑到随机（而不是确定性）网络动态，这对于模拟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：非凸性在实际网络中持续存在，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，过去的大多数结果仅针对凸情况得出。这项研究探索了对非凸性具有鲁棒性的架构选择。