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

NETS：小型：多云运营商级虚拟网络中的故障和性能管理

• 批准号: 1718929
• 负责人: Raj Jain
• 金额: $ 30万
• 依托单位: Washington University
• 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=1718929&HistoricalAwards=false
• 关键词: NETS; SMALL; Fault; Performance; Management

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.

该项目旨在开展研究，开发虚拟运营商网络的智能故障、配置和性能管理框架。研究活动将考虑改进多云环境中的管理框架，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，项目团队将应用新颖的机器学习 (ML) 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) 采用经典机器学习算法来针对非常动态、分布式和实时的网络故障。时间环境； 2）开发演绎预测故障和性能检测及定位技术； 3）开发一种混合“集中分布式”技术来训练机器学习模型。项目的一个关键成果是加深对网络功能虚拟化 (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。

项目成果

Safety Score as an Evaluation Metric for Machine Learning Models of Security Applications

安全评分作为安全应用机器学习模型的评估指标

• DOI: 10.1109/lnet.2020.3016583
• 发表时间: 2020-12
• 期刊: IEEE Networking Letters
• 作者: Salman, Tara;Ghubaish, Ali;Unal, Devrim;Jain, Raj
• 通讯作者: Jain, Raj

The P-ART framework for placement of virtual network services in a multi-cloud environment

用于在多云环境中放置虚拟网络服务的 P-ART 框架

• DOI: 10.1016/j.comcom.2019.03.003
• 发表时间: 2019-05-01
• 期刊: Comput. Commun.
• 作者: Lav Gupta;R. Jain;A. Erbad;D. Bhamare
• 通讯作者: D. Bhamare

TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security

TRUST XAI：人工智能的模型无关解释以及 IIoT 安全案例研究

• DOI: 10.1109/jiot.2021.3122019
• 发表时间: 2021-10
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Zolanvari, Maede;Yang, Zebo;Khan, Khaled;Jain, Raj;Meskin, Nader
• 通讯作者: Meskin, Nader

Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes

基于资源和拓扑属性高效配置5G核心网切片

• DOI: 10.3390/app9204361
• 发表时间: 2019-10-16
• 期刊: Applied Sciences
• 作者: Xin Li;Chengcheng Guo;Jun Xu;Lav Gupta;R. Jain
• 通讯作者: R. Jain

Fault and performance management in multi-cloud virtual network services using AI: A tutorial and a case study

使用 AI 进行多云虚拟网络服务中的故障和性能管理：教程和案例研究

• DOI: 10.1016/j.comnet.2019.106950
• 发表时间: 2019-12
• 期刊: Computer Networks
• 影响因子: 5.6
• 作者: Gupta, Lav;Salman, Tara;Zolanvari, Maede;Erbad, Aiman;Jain, Raj
• 通讯作者: Jain, Raj