权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

NeTS: Small: Modeling and Alleviating Physical Impairments in Translucent EONs

NeTS：小型：半透明 EON 中的建模和减轻物理损伤

基本信息

批准号：
1718130
负责人：
Maite Brandt-Pearce
金额：
$ 35万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718130&HistoricalAwards=false
关键词：
NeTS Small Modeling Alleviating Physical

项目摘要

Society has shown an endless appetite for new data-rich applications running over the Internet, which depends on fiber-optic networks to carry its traffic. Elastic optical networks (EONs) are widely seen as the logical evolution of current large-scale fiber-optic core transport networks due to their flexibility and ability to support growing capacity demands. They can also deploy advanced signal processing techniques to balance the requirements of high resource efficiency and high signal quality. The goal of this proposed work is to design long-haul EONs and invent algorithms that wisely employ its system resources, including the optical spectrum as well as physical network equipment. The objective is to minimize resource usage, implying low capital expenditure and low operational cost, while satisfying expected increasing traffic needs. Better network design and dimensioning algorithms will enable providers to meet their customers demands far more quickly and cost-effectively. The research entails using an accurate model of physical-layer impairments within the design and management of translucent EONs. By using a network-state-dependent performance prediction instead of the transmission reach in the routing and resource allocation procedure, significant spectral and equipment savings can be gained. This cross-layer approach trades off computational complexity for increased resource usage efficiency. The research agenda begins by modifying the currently popular Gaussian-noise model for physical layer impairments for modern long-haul networks to be easily computed as a function of the network state. Then the project addresses the design of long-haul EONs by solving two fundamental problems:- Algorithm design for routing and resource allocation based on accurate physical impairment estimation; classical approaches to solving this problem have relied on worst case estimates that significantly overprovision the network. - Theoretical models for EON behavior in the presence of network-dependent physical layer impairments;the goal is to abstract physical layer parameters to make resource allocation calculations more tractable and less sensitive to small changes in those parameters. The project will be performed in collaboration with a research team at Nokia Bell Labs to ensure industrial relevance of the work.

社会对在互联网上运行的新的数据丰富的应用程序表现出无限的兴趣，互联网依赖于光纤网络来承载其流量。弹性光网络（EON）由于其灵活性和支持不断增长的容量需求的能力而被广泛视为当前大规模光纤核心传输网络的逻辑演进。他们还可以部署先进的信号处理技术，以平衡高资源效率和高信号质量的要求。这项工作的目标是设计长距离的EON，并发明算法，明智地利用其系统资源，包括光谱以及物理网络设备。其目标是最大限度地减少资源使用，这意味着低资本支出和低运营成本，同时满足预期增长的交通需求。更好的网络设计和规模算法将使供应商能够更快、更经济地满足客户的需求。该研究需要在半透明EON的设计和管理中使用物理层损伤的精确模型。通过在路由和资源分配过程中使用依赖于网络状态的性能预测而不是传输范围，可以获得显著的频谱和设备节省。这种跨层方法在计算复杂性和提高资源使用效率之间进行了权衡。研究议程首先修改了目前流行的高斯噪声模型的物理层损伤的现代长途网络，可以很容易地计算为网络状态的函数。然后，该项目通过解决两个基本问题来解决长途EONs的设计：-基于准确的物理损伤估计的路由和资源分配算法设计;解决这个问题的经典方法依赖于最坏情况下的估计，显着过度供应网络。 - 在存在网络相关物理层损伤的情况下EON行为的理论模型;目标是抽象物理层参数，使资源分配计算更易于处理，对这些参数的微小变化不太敏感。该项目将与诺基亚贝尔实验室的研究团队合作进行，以确保工作的工业相关性。