TF:SING: Exploring the Fundamental Limits of Cross-Layer Optimization in the Next-Generation Internet

虽然已经对跨层优化进行了大量的研究，但对跨层优化的设计极限仍缺乏基本的认识。这个项目的首要目标是填补这一知识空白。本研究的目标是：1)定性地描述跨层优化的多尺度时空行为；2)开发一种新的复杂性可扩展的框架，以支持具有不同计算能力的异质网元的跨层优化；3)在现有测试平台的基础上，通过大量的模拟和现场测试来评估所提出的框架。该项目的智力优势和变革意义包括：1)为理解跨层设计和优化中的主要设计权衡提供理论框架；2)为定量评估和比较不同的跨层设计方案提供共同点。通过开发新的理论框架来定量分析复杂系统优化中的设计权衡，该项目的更广泛影响是：1)造福于社会网络、经济学、物理学和生物学的广泛应用，对社会各个层面产生深刻影响；2)扩大与行业合作伙伴的现有合作，并加强STEM教育方面的持续努力。结果、结果、软件工具、基准和教育材料将通过S项目网站以及期刊和会议出版物传播。跨层优化的研究成果也将整合到PI大学教授的课程中。

Although there has been a considerable amount of research done by using cross-layer optimization, fundamental understandings on the design limits of cross-layer optimization are still missing. The overarching goal of this project is to fill this knowledge gap. The objectives are to 1) qualitatively characterize multi-scale temporal-spatial behaviors of cross-layer optimization; 2) develop a novel complexity-scalable framework to support cross-layer optimization over heterogeneous network elements with different computation capabilities; and 3) evaluate the proposed framework by using extensive simulations as well as field test based on our existing testbed.The intellectual merits and transformative significance of this project include: 1) to provide a theoretical framework to understand major design tradeoffs in cross-layer design and optimization and 2) to provide a common ground to quantitatively evaluate and compare different cross-layer design schemes. By developing a new theoretical framework to quantitatively analyze design tradeoffs in complex system optimization, the broader impacts of this project are: 1) to benefit wide applications in social networks, economics, physics, and biology, leading to a profound impact on all societal levels and 2) to expand the existing collaborations with industrial partners and enhance the ongoing effort on STEM education. Results, outcomes, software tools, benchmarks, and educational materials will be disseminated through a project?s web site as well as journal and conference publications. The research results of cross-layer optimizations will also be integrated into courses taught in the PI's university.