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

第一部分：未来的光网络数据速率将有数量级的增长，这至少部分归因于大数据交易的增加。这就产生了对网络资源的快速调度和灵活的网络适应的需求，以最高效地在网络中移动数据。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，并使用这些信息来满足总体性能目标。该项目将是第一个全面研究认知光网络管控的项目。目标是实现灵活的自动适应，以取代目前缓慢的、手动驱动的管理和控制做法。这项研究的成果将对下一代无线网络和电网系统以及对可能严重扰乱网络的极端事件的快速检测产生影响。第二部分：目前的光网络主要采用静态连接，光路是准静态改变的，通常几个月都保持不变。当前建立波长路径的方法导致对网络的缓慢改变(~20分钟建立时间)，因为沿着该路径的每个网元被逐渐调谐以避免由于快速将另一个光信道引入网络而引起的不稳定性和瞬时损伤。大流量和其他流量动态将需要大约100毫秒-1秒的带宽调整。在今天的光网络中，当光路分几步开启时，所有相邻波长的链路质量都受到监控。在动态未来网络中，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现实。这个项目的想法是感知和控制信息量最大的一小部分参数，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运行网络的认知技术，以及推断网络状态和优化性能，而不会给网络带来不必要的网络控制和管理通信负担。本研究将同时考虑动态每会话调度、重路由/负载均衡和拓扑重新配置来优化网络性能。

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.