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

未来的光网络将在数据速率上有数量级的增长，至少部分原因是大数据交易的增加。这就需要快速调度网络资源和灵活的网络自适应，以最有效地在网络中移动数据。 本计画提出研究一种认知网路管理与控制系统，它能“感知”目前的网路状况，并利用这些资讯来达成整体效能目标。本项目将是第一个对认知光网络管理和控制进行全面研究的项目。我们的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。当前的光网络主要以静态连接运行，光路准静态地变化，并且通常数月保持不变。建立波长路径的现有方法导致网络的缓慢变化（~20分钟的建立时间），因为沿着路径的每个网络元件沿着被逐渐调谐以避免由于快速地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约100 mS-1 S量级的带宽适应。 在当今的光网络中，当光路在几个步骤中被打开时，所有相邻波长的链路质量被监控。在动态未来网络中，网络参数的数量和它们的短相干时间使得获取网络的完整状态信息变得不切实际。这个项目的想法是感测和控制一小部分具有“最大”信息内容的参数，并利用它们之间的关系来调整网络。这种策略中需要包含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，以及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。本研究将同时考虑动态每会话调度，重路由/负载平衡和拓扑重构，以优化网络性能。

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.