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.

该项目试图进行研究，从而为虚拟载体网络开发智能故障，配置和性能管理框架。研究活动将考虑在多个云设置中改进管理框架，以便可以实现运营商等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。研究活动具有3个主要推力：1）调整经典的机器学习算法以针对一个非常动态的，分布式和实时环境； 2）开发推论性预测性断层，性能检测和定位技术； 3）开发一种用于训练机器学习模型的混合“中央分布”技术。一个关键的项目结果是增强对网络功能虚拟化（NFV）管理，云管理和操作支持的理解，可以利用机器学习。研究团队将开发用于培训机器学习模型的新技术，并将开发一个测试台以验证该方法。

项目成果

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

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

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
• 期刊: IEEE Networking Letters
• 作者: Salman, Tara;Ghubaish, Ali;Unal, Devrim;Jain, Raj
• 通讯作者: Jain, Raj

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

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

HYPER-VINES: A HYbrid Learning Fault and Performance Issues ERadicator for Virtual NEtwork Services over Multi-Cloud Systems

HYPER-VINES：用于多云系统上的虚拟网络服务的混合学习故障和性能问题 ERadicator

• DOI: 10.1109/iccnc.2019.8685496
• 发表时间: 2019
• 期刊: Networking and Communications (ICNC
• 作者: Gupta, Lav;Salman, Tara;Das, Ria;Erbad, Aiman;Jain, Raj;Samaka, Mohammed
• 通讯作者: Samaka, Mohammed