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

新兴的5G网络预计将服务于医疗、教育、交通和制造业等不同行业的一系列关键应用。支持如此广泛的应用程序时的一个主要挑战是，在出现网络故障和其他不断变化的网络条件的情况下，能够提供高度的可用性并保持服务要求。5G网络中的网络切片概念被广泛认为是一种很有前途的方法，可以通过创建特定于应用的网络切片来支持不同的服务需求。网络切片由虚拟计算和网络资源组成，这些资源是为各个应用程序的独特需求量身定做的。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随时间动态调整，以响应网络故障或网络条件的变化。该项目旨在通过弹性网络切片的智能设计、配置和恢复来解决5G网络中维护高可用性和多样化服务需求的问题。这项工作将产生提供健壮和弹性的网络基础设施的技术，能够在动态和不利条件下支持关键应用。该项目的主要目标是开发方法和技术，以维护5G网络中弹性网络切片的高可用性和多样化的服务需求。为了实现这些目标，本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，用于在保持片隔离和服务要求的同时对弹性网络片进行定价和准入控制；2)用于合成网络片并将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的情况下提供可用性保证；以及3)用于在灾难性故障事件之后逐步恢复虚拟和物理基础设施组件的方案。方法论将在在线凸优化的背景下开发，这将为一类离散优化问题提供一种获得理论性能界限的手段。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，以评估和验证建议的计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.