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CNS Core: Small: Design and Evaluation of Methods for Supporting Resilient and High-Availability Elastic Network Slicing

CNS Core：小型：支持弹性和高可用性弹性网络切片的方法设计和评估

基本信息

批准号：
2008856
负责人：
Jason Jue
金额：
$ 44.97万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008856&HistoricalAwards=false
关键词：
CNS Core Small Design Evaluation

项目摘要

Emerging 5G networks are expected to serve an array of critical applications in different industries such as healthcare, education, transportation, and manufacturing. A major challenge when supporting such a wide range of applications is being able to provide a high degree of availability and maintaining service requirements in the presence of network failures and other constantly changing network conditions. The concept of network slicing in 5G networks is widely viewed as a promising approach for supporting diverse service requirements by enabling the creation of application-specific network slices. A network slice consists of virtual computing and network resources that are tailored to the unique requirements of individual applications. Of particular interest is the concept of elastic network slicing in which the resources allocated to a slice may be dynamically adjusted over time in response to network failures or changes in network conditions. This project aims to address the issues of maintaining high availability and diverse service requirements in 5G networks through the intelligent design, provisioning, and restoration of elastic network slices. The work will result in techniques for providing a robust and resilient network infrastructure that is able support critical applications under dynamic and adverse conditions.The primary goals of this project are to develop methods and techniques for maintaining high availability and diverse service requirements for elastic network slices in 5G networks. To achieve these goals, this project considers a framework that utilizes techniques from deep reinforcement learning and online convex optimization to develop schemes for deploying, provisioning, reconfiguring, and restoring elastic network slices. Specific research problems that will be addressed include 1) machine learning based strategies for the pricing and admission control of elastic network slices while maintaining slice isolation and service requirements, 2) schemes for composing network slices and mapping slices to a physical infrastructure to optimize cost while providing availability guarantees in the presence of infrastructure failures, and 3) schemes for the progressive recovery of virtual and physical infrastructure components after catastrophic failure events. Methodologies will be developed in the context of online convex optimization that will provide a means to obtain theoretical performance bounds for a class of discrete optimization problems. The proposed techniques will be rigorously evaluated through comprehensive analytical models and simulation experiments. Testbed activities will be initiated to evaluate and validate proposed schemes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

新兴的5G网络预计将服务于不同行业的一系列关键应用，如医疗保健、教育、交通和制造业。在支持如此广泛的应用时，一个主要的挑战是能够提供高度的可用性，并在出现网络故障和其他不断变化的网络条件下保持服务要求。5G网络中的网络切片概念被广泛认为是一种有前途的方法，通过创建特定于应用的网络切片来支持各种服务需求。网络切片由虚拟计算和网络资源组成，这些资源是根据各个应用程序的独特要求定制的。特别感兴趣的是弹性网络切片的概念，其中分配给切片的资源可以响应于网络故障或网络条件的变化而随时间动态地调整。该项目旨在通过弹性网络切片的智能设计、配置和恢复，解决5G网络中保持高可用性和多样化服务需求的问题。该项目的主要目标是开发用于在动态和不利条件下支持关键应用的强大和弹性网络基础设施的技术，以保持5G网络中弹性网络切片的高可用性和多样化服务需求。为了实现这些目标，该项目考虑了一个框架，该框架利用深度强化学习和在线凸优化技术来开发部署、配置、重新配置和恢复弹性网络切片的方案。将解决的具体研究问题包括：1）基于机器学习的策略，用于弹性网络切片的定价和准入控制，同时保持切片隔离和服务要求，2）用于组成网络切片并将切片映射到物理基础设施以优化成本的方案，同时在存在基础设施故障时提供可用性保证，以及3）灾难性故障事件后逐步恢复虚拟和物理基础设施组件的方案。将开发在线凸优化的背景下，将提供一种手段来获得一类离散优化问题的理论性能界限的方法。所提出的技术将通过全面的分析模型和模拟实验进行严格的评估。将启动试验台活动，以评估和验证拟议的计划。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Availability-guaranteed slice composition for service function chains in 5G transport networks

DOI：
10.1364/jocn.403721
发表时间：
2020-11
期刊：
IEEE/OSA Journal of Optical Communications and Networking
影响因子：
0
作者：
Riti Gour;Genya Ishigaki;Jian Kong;J. Jue
通讯作者：
Riti Gour;Genya Ishigaki;Jian Kong;J. Jue

Reinforcement Learning-Based Network Slice Resource Allocation for Federated Learning Applications

DOI：
10.1109/globecom48099.2022.10001715
发表时间：
2022-12
期刊：
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
影响因子：
0
作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;J. Jue
通讯作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;J. Jue

A Reinforcement Learning-Based Routing Strategy for Elastic Network Slices

基于强化学习的弹性网络切片路由策略

DOI：
发表时间：
2022
期刊：
ICC 2022 - IEEE International Conference on Communications
影响因子：
0
作者：
Wu, Zhouxiang;Jue, Jason P.
通讯作者：
Jue, Jason P.

Reinforcement Learning-Based Multi-Domain Network Slice Provisioning

DOI：
10.1109/icc45041.2023.10278745
发表时间：
2023-05
期刊：
ICC 2023 - IEEE International Conference on Communications
影响因子：
0
作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;Feng Mi;Subhash Talluri;Jason P. Jue
通讯作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;Feng Mi;Subhash Talluri;Jason P. Jue

Traffic-Weighted Availability-Guaranteed Network Slice Composition with VNF Replications