NeTS: Small: Cognitive Management and Control of Agile Dynamic Optical Networks

NeTS：小型：敏捷动态光网络的认知管理和控制

• 批准号: 1717199
• 负责人: Vincent Chan
• 金额: $ 49.9万
• 依托单位: Massachusetts Institute of Technology
• 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: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717199&HistoricalAwards=false
• 关键词: NeTS; Small; Cognitive; Management; Control

Part 1.The optical network of the future will have orders of magnitude increase in data rates, due at least partially to the increase in big-data transactions. These create the need for fast scheduling of network resources and agile network adaptation to most efficiently move the data across the network. This project proposes to investigate a cognitive network management and control system, which 'senses' current network conditions and uses this information to satisfy overall performance goals. This project will be the first comprehensive research on cognitive optical network management and control. The goal is for agile automated adaptation to replace current slow, manually-driven management and control practices. The fruits of this research will have implications for next generation wireless networks and power grid systems and for fast detection of extreme events that can significantly disrupt networks. Part 2.Current optical networks are operated with predominantly static connections, with lightpaths changed quasi-statically and often remaining unchanged for months. Present methods of setting up a wavelength path result in slow changes to the network (~20 min setup times), as each of the network elements along the path is gradually tuned to avoid instabilities and transient impairments arising from rapidly introducing another optical channel into the network. Large flow and other traffic dynamics will require bandwidth adaptations of the order of ~100mS-1S. In today's optical networks, the link quality of all adjacent wavelengths is monitored as the lightpath is turned on in several steps. The number of network parameters and their short coherence time in dynamic future networks render acquiring the complete state information of the network impractical. The idea of this project is to sense and control a small subset of the parameters with the 'largest' information content and use their relationship to tune the network. Cognitive techniques that need to be incorporated in such a strategy include sampling of network and link parameters, applying data analytics to learn behavior and cognitive techniques to operate networks, and inferring network state and optimizing performance without placing an undue network control and management communications burden on the network. This research will consider both dynamic per-session scheduling, rerouting/load-balancing and topology reconfiguration to optimize network performance.

第 1 部分：未来光网络的数据速率将呈数量级增长，这至少部分归因于大数据交易的增加。这就需要快速调度网络资源和灵活的网络适应，以最有效地在网络上移动数据。 该项目建议研究一种认知网络管理和控制系统，该系统“感知”当前的网络状况并使用此信息来满足总体性能目标。该项目将是第一个关于认知光网络管理和控制的综合研究。目标是实现敏捷的自动化适应，以取代当前缓慢的、手动驱动的管理和控制实践。这项研究的成果将对下一代无线网络和电网系统以及快速检测可能严重破坏网络的极端事件产生影响。第 2 部分：当前光网络主要通过静态连接运行，光路准静态变化，并且通常数月保持不变。目前设置波长路径的方法会导致网络变化缓慢（约 20 分钟设置时间），因为沿路径的每个网络元件都会逐渐调整，以避免因快速将另一个光通道引入网络而产生的不稳定和瞬态损伤。网络。大流量和其他流量动态将需要 ~100mS-1S 量级的带宽调整。 在当今的光网络中，当光路分几个步骤打开时，所有相邻波长的链路质量都会受到监控。动态未来网络中网络参数的数量及其短的相干时间使得获取网络的完整状态信息变得不切实际。该项目的想法是感知和控制具有“最大”信息内容的一小部分参数，并利用它们的关系来调整网络。需要纳入此类策略的认知技术包括对网络和链路参数进行采样、应用数据分析来学习行为和认知技术来操作网络、推断网络状态和优化性能，而不会给网络带来过度的网络控制和管理通信负担。网络。这项研究将考虑动态每会话调度、重新路由/负载平衡和拓扑重新配置，以优化网络性能。

项目成果

Cognitive Management and Control for Wavelength Assignment and Reconfiguration in Optical Networks

光网络中波长分配和重新配置的认知管理和控制

• 发表时间: 2019-07
• 期刊: IEEE International Conference on Communications
• 作者: Zheng, Anny Xijia;Chan, Vincent
• 通讯作者: Chan, Vincent

Diversity Routing to Improve Delay-Jitter Tradeoff in Uncertain Network Environments

分集路由可改善不确定网络环境中的延迟抖动权衡

• 发表时间: 2019-07
• 期刊: IEEE International Conference on Communications
• 作者: Rezaee, Arman;Chan, Vincent
• 通讯作者: Chan, Vincent

Cognitive Network Management and Control with Significantly Reduced State Sensing

显着减少状态感知的认知网络管理和控制

• DOI: 10.1109/glocom.2018.8648066
• 发表时间: 2018-12
• 期刊: 2018 IEEE Global Communications Conference (GLOBECOM
• 作者: Rezaee, Arman;Chan, Vincent W.
• 通讯作者: Chan, Vincent W.

Resilient Optical Networks

弹性光网络

• 发表时间: 2018-07
• 期刊: International Conference on Transparent Optical Networks
• 作者: Chan; Vincent
• 通讯作者: Vincent

Cognitive Network Management and Control with Significantly Reduced State Sensing

显着减少状态感知的认知网络管理和控制

• DOI: 10.1109/tccn.2019.2918288
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: Rezaee, Arman;Chan, Vincent W.
• 通讯作者: Chan, Vincent W.