NETS: SMALL: Fault and Performance Management in Carrier-Grade Virtual Networks Over Multiple Clouds

该项目旨在开展研究，为虚拟运营商网络开发智能故障、配置和性能管理框架。研究活动将考虑在多云环境中改进管理框架，以便实现运营商级网络的高可用性和弹性要求。为了实现这一目标，该项目团队将应用新型机器学习算法来执行网络故障检测和隔离。研究活动有3个主要目标：1）使经典机器学习算法适应非常动态、分布式和实时的环境; 2）开发演绎预测故障和性能检测和定位技术;以及3）开发用于训练机器学习模型的混合“集中-分布式”技术。一个关键的项目成果是提高对网络功能虚拟化（NFV）管理、云管理和运营支持如何利用机器学习的理解。研究团队将开发用于训练机器学习模型的新技术，并将开发一个测试平台来验证该方法。

This project seeks to carry out research leading to the development of an intelligent fault, configuration and performance management framework for virtual carrier networks. The research activity will consider improvements to the management framework in a multiple cloud setting so that the high availability and resiliency requirements of carrier grade networks can be realized. To accomplish this objective,the project team will apply novel Machine Learning (ML) algorithms to perform network fault detection and isolation.The research activity has 3 main thrusts: 1) adapting classical machine learning algorithms to target a very dynamic, distributed and real-time environment; 2) developing deductive-predictive fault and performance detection and localization techniques; and 3) developing a hybrid 'central-distributed' technique for training the machine learning models. A key project outcome is to enhance understanding of how Network Function Virtualization (NFV) management, cloud management and operations support can leverage machine learning. The research team will develop new techniques for training machine learning models, and a testbed will be developed to validate the approach.