NeTS: Small: Collaborative Research: Towards Adaptive and Efficient Wireless Computing Networks

NeTS：小型：协作研究：迈向自适应且高效的无线计算网络

• 批准号: 1813392
• 负责人: Lei Ying
• 金额: $ 25万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813392&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Towards

Today's mobile devices are not merely smart, they are becoming intelligent as artificial intelligence applications such as Facebook Caffe2 and Google Tensor-Flow Lite are being pushed into mobile devices and as mobiles devices are being integrated into the cloud-fog-mobile architecture. This calls for efficient and adaptive computing/communication co-design of wireless networks to optimize application-level latency (including both communication latency and computing times) and to achieve energy efficiency (considering energy consumed by both communications and computing). This project develops fundamental theories and novel architectures of low-latency, energy-efficient, and computing-centric wireless networks to support emerging mobile intelligence applications. Theories and algorithms developed by the PIs are constantly integrated into the undergraduate and graduate courses taught at the two universities. This project also provides hand-on experiences to undergraduate and high school students with state-of-the-art wireless technologies. Computing/communication co-design, while new for wireless networks, is a central topic in data center networks. However, the proposed solutions, while inspiring, are not directly applicable to wireless computing networks because of the unique features of wireless networks such as wireless interference, channel fading and limited energy. This project focuses on provably optimal mechanisms that dynamically and adaptively schedule computing tasks and data transmissions to meet application-level performance requirements, and consists of three interdependent thrusts: (i) Optimal computing/communication co-design. This thrust develops mathematical models and theoretical limits of wireless computing networks, (ii) Robust computing/communication co-design. This thrust focuses on robust computing and communication co-design that achieves desired performance with imperfect state information and under unavoidable short-term system overload and (iii) Learning-aided adaptive computing/communication co-design. This thrust further improves the performance of wireless computing networks by leveraging both historical data and predictable user behaviors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当今的移动设备不仅是聪明的，而且随着Facebook Caffe2和Google Tensor-Flow Lite等人工智能应用程序被推到移动设备上，并且随着手机设备被集成到云模具模具式架构中，它们也变得聪明。这要求无线网络的有效和自适应计算/通信共同设计优化应用程序级别的延迟（包括通信延迟和计算时间）并实现能源效率（考虑到通信和计算所消耗的能源）。 该项目开发了低延迟，节能和以计算为中心的无线网络的基本理论和新颖体系结构，以支持新兴的移动智能应用程序。 PIS开发的理论和算法经常融入两所大学的本科和研究生课程中。该项目还为本科和高中生提供了最先进的无线技术的手工经验。计算/通信共同设计，而新的无线网络是数据中心网络中的一个核心主题。但是，由于无线网络的独特功能，例如无线干扰，通道褪色和有限的能量，拟议的解决方案虽然鼓舞人心，但并不直接适用于无线计算网络。该项目侧重于在动态和适应地安排计算任务和数据传输以满足应用程序级的性能要求的最佳机制上，并由三个相互依存的推力组成：（i）最佳计算/通信共同设计。这种推力开发了无线计算网络的数学模型和理论限制，（ii）强大的计算/通信共同设计。这种推力重点是可靠的计算和通信共同设计，该设计通过不完美的状态信息以及不可避免的短期系统过载和（iii）学习辅助自适应计算/通信共同设计实现所需的性能。这种推力通过利用历史数据和可预测的用户行为来进一步提高无线计算网络的性能。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来获得支持的。

项目成果

A Mean Field Game Analysis of Distributed MAC in Ultra-Dense Multichannel Wireless Networks

• DOI: 10.1145/3323679.3326498
• 发表时间: 2019-07
• 期刊: IEEE/ACM Transactions on Networking
• 作者: D. Narasimha;S. Shakkottai;Lei Ying