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: 2002608
• 负责人: Lei Ying
• 金额: $ 24.24万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-12 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002608&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.

今天的移动设备不仅仅是智能，而且随着Facebook Caffe2和Google Tensor-Flow Lite等人工智能应用程序被推入移动设备，以及移动设备被集成到云-雾-移动架构中，它们正在变得智能化。这就需要无线网络的高效和自适应计算/通信协同设计，以优化应用级延迟（包括通信延迟和计算时间）并实现能源效率（考虑通信和计算消耗的能源）。 该项目开发低延迟、节能和以计算为中心的无线网络的基础理论和新颖架构，以支持新兴的移动智能应用。 PI 开发的理论和算法不断融入两所大学教授的本科生和研究生课程中。该项目还为本科生和高中生提供最先进的无线技术的实践经验。计算/通信协同设计虽然对于无线网络来说是新事物，但却是数据中心网络的中心主题。然而，所提出的解决方案虽然鼓舞人心，但由于无线网络的独特特征（例如无线干扰、信道衰落和有限能量），并不直接适用于无线计算网络。该项目重点关注可证明的最佳机制，动态和自适应地调度计算任务和数据传输以满足应用程序级性能要求，并由三个相互依赖的主旨组成：（i）最佳计算/通信协同设计。这一推动力开发了无线计算网络的数学模型和理论极限，(ii) 鲁棒计算/通信协同设计。这一主旨侧重于鲁棒的计算和通信协同设计，以在不完美的状态信息和不可避免的短期系统过载的情况下实现所需的性能，以及（iii）学习辅助的自适应计算/通信协同设计。这一推动力通过利用历史数据和可预测的用户行为进一步提高了无线计算网络的性能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Distributed Threshold-based Offloading for Large-Scale Mobile Cloud Computing

用于大规模移动云计算的分布式基于阈值的卸载

• DOI: 10.1109/infocom42981.2021.9488821
• 发表时间: 2021-05
• 期刊: IEEE International Conference on Computer Communications(INFOCOM
• 作者: Qin, Xudong;Li, Bin;Ying, Lei
• 通讯作者: Ying, Lei

An Optimal Stopping Approach for Iterative Training in Federated Learning

联邦学习迭代训练的最优停止方法

• DOI: 10.1109/ciss48834.2020.1570616094
• 发表时间: 2020-03-01
• 期刊: 2020 54th Annual Conference on Information Sciences and Systems (CISS)
• 作者: Pengfei Jiang;Lei Ying
• 通讯作者: Lei Ying

Steady‐state analysis of load balancing with Coxian‐2 distributed service times

使用 Coxian™2 分布式服务时间进行负载平衡的稳态分析

• DOI: 10.1002/nav.21986
• 发表时间: 2021-03
• 期刊: Naval Research Logistics (NRL
• 作者: Liu, Xin;Gong, Kang;Ying, Lei
• 通讯作者: Ying, Lei

A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes

无限视野平均奖励约束马尔可夫决策过程的可证明有效的无模型算法

• DOI: 10.1609/aaai.v36i4.20302
• 发表时间: 2022-06
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Wei, Honghao;Liu, Xin;Ying, Lei
• 通讯作者: Ying, Lei

Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation

Triple-Q：一种用于具有次线性遗憾和零约束违规的约束强化学习的无模型算法

• 发表时间: 2022-01
• 期刊: Proceedings of The 25th International Conference on Artificial Intelligence and Statistics
• 作者: Wei, H;Liu, X;Ying, L
• 通讯作者: Ying, L