NeTS: Small: Collaborative Research: Lightweight Adaptive Algorithms for Network Optimization at Scale towards Emerging Services

NetS：小型：协作研究：面向新兴服务的大规模网络优化的轻量级自适应算法

• 批准号: 1814614
• 负责人: My Thai
• 金额: $ 27.2万
• 依托单位: University of Florida
• 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 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814614&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Lightweight

The past decade has witnessed enormous transformations of information technologies. The era of cloud computing transformed how information is delivered. With more and more devices becoming ``smarter'' and ``plugged'' into the Internet (through embedded microprocessors and communication chipsets), we are entering into a new era of ``Internet of Things'' (IoT) and cyber-physical systems where the cyber world and physical world are increasingly integrated through a variety of sensors and actuators that are planted into the physical world but controlled through the cyber world. Both today's cloud services and emerging IoT applications alter the point-to-point communication paradigm of the existing IP Internet architecture, and require increasingly demanding quality-of-service (QoS) requirements. These requirements call for scalable and intelligent network algorithms for controlling and coordinating various network components and managing and optimizing resource allocations, with capabilities 1) to meet ever stringent availability, reliability and QoS requirements demanded by emerging services; 2) to cope with the enormous complexity of networked systems; 3) to effectively exploit the rich diversity and redundancy inherent in such complex systems as well as the new capabilities offered by new networking architectures and technologies. This project puts forth a three-plane view of networking as a conceptual framework to structure network functions and guide us in the network algorithmic designs for timely, resilient and resource-efficient information delivery: 1) an information plane capturing application semantics and requirements; 2) a (logically) centralized control plane; and 3) a distributed (programmable) communication (data) plane. This project postulates two design principles and challenges in network algorithms: a) the need for co-design of centralized and distributed network algorithms that can take advantage of a centralized control plane with a global view of the network state, while also enabling the distributed (programmable) network elements to make fast and intelligent decisions to adapt to the changes in the network conditions (e.g., failures); and b) the need for just-in-time (near) optimality as a key metric to gauge and guide the design of network algorithms for timely, resilient and resource-efficient information delivery. The goal of the proposed project is to develop scalable, lightweight, and adaptive algorithms for scalable and intelligent network control and optimization. The research on new network algorithms will contribute to the development of new networking technologies. The project will actively involve undergraduate students - especially female students and underrepresented minorities in the research project, e.g., via NSF REU grants as well as senior design courses and the Undergraduate Research Opportunity Programs at the PIs' institutions.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.

过去十年见证了信息技术的巨大变革。云计算时代改变了信息的传递方式。随着越来越多的设备变得“智能”和“接入”互联网（通过嵌入式微处理器和通信芯片组），我们正在进入一个“物联网”（IoT）和网络物理系统的新时代，通过各种传感器和执行器，将网络世界和物理世界日益融合，这些传感器和执行器植入物理世界，但通过网络世界进行控制。当今的云服务和新兴的物联网应用都改变了现有IP互联网架构的点对点通信范式，并且对服务质量（QoS）的要求越来越高。这些需求需要可扩展的智能网络算法来控制和协调各种网络组件，管理和优化资源分配，并具备以下能力：1)满足新兴业务对日益严格的可用性、可靠性和QoS要求；2)应对网络系统的巨大复杂性；3)有效利用这些复杂系统所固有的丰富多样性和冗余性，以及新的网络架构和技术所提供的新功能。本项目提出了网络的三平面视图，作为构建网络功能的概念框架，并指导我们进行网络算法设计，以实现及时、弹性和资源高效的信息传递：1)捕获应用语义和需求的信息平面；2)（逻辑上）集中控制平面；3)分布式（可编程）通信（数据）平面。本项目提出了网络算法的两个设计原则和挑战：a)需要集中和分布式网络算法的协同设计，这些算法可以利用具有网络状态全局视图的集中控制平面，同时也使分布式（可编程）网络元素能够做出快速和智能的决策，以适应网络条件的变化（例如，故障）；b)需要及时（接近）最优性作为衡量和指导网络算法设计的关键指标，以实现及时、有弹性和资源高效的信息传递。该项目的目标是为可扩展的智能网络控制和优化开发可扩展的、轻量级的和自适应的算法。新的网络算法的研究将促进新的网络技术的发展。该项目将通过NSF REU拨款、高级设计课程和pi所在机构的本科生研究机会计划等方式积极吸引本科生参与，尤其是女生和少数族裔。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

Vulnerability Assessment of Social-Smart Grids: An Algorithmic Approach

• DOI: 10.1109/globecom38437.2019.9014195
• 发表时间: 2019-12
• 期刊: 2019 IEEE Global Communications Conference (GLOBECOM)
• 作者: Lan N. Nguyen;J. D. Smith;M. Thai

Influence Diffusion in Online Social Networks With Propagation Rate Changes

• DOI: 10.1109/tnse.2020.3015935
• 发表时间: 2020-10
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Tianyi Pan;Xiang Li;Alan Kuhnle;M. Thai

Efficient Algorithms for Monotone Non-Submodular Maximization with Partition Matroid Constraint

具有划分拟阵约束的单调非子模最大化的高效算法

• DOI: 10.24963/ijcai.2022/666
• 发表时间: 2022
• 期刊: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22
• 作者: Nguyen, L;Thai, MT
• 通讯作者: Thai, MT

SaPHyRa: A Learning Theory Approach to Ranking Nodes in Large Networks

SaPHyRa：大型网络中节点排名的学习理论方法

• DOI: 10.1109/icde53745.2022.00009
• 发表时间: 2022
• 期刊: 2022 IEEE 38th International Conference on Data Engineering (ICDE
• 作者: Thai, P;Thai, MT;Dinh, T
• 通讯作者: Dinh, T

Stability and Generalization of Graph Convolutional Neural Networks

• DOI: 10.1145/3292500.3330956
• 发表时间: 2019-05
• 期刊: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Saurabh Verma;Zhi-Li Zhang