NeTS: Small: Collaborative Research: Lightweight Adaptive Algorithms for Network Optimization at Scale towards Emerging Services
NeTS: Small: Collaborative Research: Lightweight Adaptive Algorithms for Network Optimization at Scale towards Emerging Services
批准号:
1814614
负责人:
My Thai
金额:
$27.2万
依托单位:
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
项目状态:
已结题
起止时间:
2018-10-01 至 2023-09-30
中文摘要
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“更智能”和“插入”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,其中网络世界和物理世界通过各种传感器和执行器越来越多地集成到物理世界中,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模式,并要求越来越苛刻的服务质量(QoS)要求。这些要求要求可扩展的和智能的网络算法,用于控制和协调各种网络组件,并管理和优化资源分配,具有以下能力:1)满足新兴服务所要求的日益严格的可用性、可靠性和QoS要求; 2)科普联网系统的巨大复杂性; 3)有效地利用这种复杂系统中固有的丰富多样性和冗余性,以及新的网络体系结构和技术提供的新功能。 该项目提出了一个网络的三个平面视图作为一个概念框架来构建网络功能,并指导我们在网络算法设计中实现及时,弹性和资源高效的信息传递:1)信息平面捕获应用程序语义和需求; 2)(逻辑上)集中式控制平面;和3)分布式(可编程)通信(数据)平面。该项目假设了网络算法中的两个设计原则和挑战:a)需要集中式和分布式网络算法的协同设计,其可以利用具有网络状态的全局视图的集中式控制平面,同时还使分布式(可编程)网络元件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);以及B)需要及时(接近)最优性作为衡量和指导用于及时、弹性和资源有效的信息递送的网络算法的设计的关键度量。 该项目的目标是为可扩展和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有助于新的网络技术的发展。该项目将积极让本科生-特别是女生和代表性不足的少数民族-参与研究项目,例如,该奖项反映了NSF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。
英文摘要
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.
期刊论文(20)
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DOI:
10.1109/globecom38437.2019.9014195
发表时间:
2019-12
期刊:
2019 IEEE Global Communications Conference (GLOBECOM)
影响因子:
--
作者:
[Lan N. Nguyen;J. D. Smith;M. Thai]
通讯作者:
Lan N. Nguyen;J. D. Smith;M. Thai
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]
通讯作者:
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
影响因子:
--
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
影响因子:
--
作者:
[Thai, P, Thai, MT, Dinh, T]
通讯作者:
Dinh, T
Network Resilience Assessment via QoS Degradation Metrics: An Algorithmic Approach
通过 QoS 降级指标进行网络弹性评估:一种算法方法
DOI:
10.1145/3311072
发表时间:
2019
期刊:
ACM SIGMETRICS performance evaluation review
影响因子:
--
作者:
[Nguyen, Lan, Thai, My T]
通讯作者:
Thai, My T
共 19 条
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