CRII: CNS: Towards Robust RAN Slicing: Theories, Algorithms, and Applications

随着新业务和互联网应用的快速增长，传统蜂窝网络面临着支持多样化应用的重大挑战。为了满足各种应用需求，无线电接入网络（RAN）切片已成为即将到来的5G时代最有前途的架构技术之一。RAN切片允许物理基础设施资源跨许多RAN切片共享，其中构建在底层物理RAN（基底）之上的每个切片是单独的逻辑网络，其提供一组服务。每个RAN切片由地理上分布在众多底层节点中的各种虚拟网络功能（VNF）构成。基板偶尔会出现故障。用于网络的RAN配置方案对于将VNF从基底节点故障中释放（将VNF重新映射/重新嵌入到活动基底节点上）是必要的。该项目旨在探索一种新的方案和算法，通过在统一的框架中解决用于切片恢复的RAN配置问题（称为RS配置）来增强RAN切片的鲁棒性。该项目将建立一个理论和计算方案，将这一见解形式化，并通过在RAN切片中有效地映射VNF，为RAN切片恢复提供有效和实用的技术。这项研究计划也将与教育相结合，开发新的课程和一系列新的案例研究类模块，在先进的网络课程，以及算法课程的设计和分析在肯尼索州立大学。将利用通过大学开展的外联方案，确保传播成果。PI还将在公共领域发布所提出的方案和算法的实现。本项目的目标是开发优化模型和方法，1）为使用RS配置构建用于RAN切片恢复优化的VNF映射计划奠定理论基础; 2）开发有效映射VNF所需的新方案和算法，最后3）应用我们的理论和算法开发来研究RAN切片和交易的鲁棒性。RAN相关网络环境中RAN切片恢复和RAN配置之间的权衡。将在数学编程和图论的背景下开发方法，这将有助于获得在计算复杂性的合理范围内执行的更有效和快速的解决方案。因此，这项研究的新方案和算法的RAN切片将提供计算基础，以建立一个强大的RAN切片，并有助于新的网络技术的发展。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

With the rapid growth of new services and Internet applications, traditional cellular networks are now faced with a major challenge of supporting diverse applications. To address the various application demands, Radio Access Network (RAN) slicing has become one of the most promising architectural technologies for the forthcoming 5G era. RAN slicing allows the physical infrastructure resources to be shared across many RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical network, which provides a set of services. Each RAN slice is constituted by various virtual network functions (VNFs) distributed geographically in numerous substrate nodes. Failures may occasionally arise from substrate. RAN configuration schemes for the network are imperative to relieve VNFs from substrate node failures (remapping/re-embedding VNFs onto live substrate nodes). This project aims to explore a new scheme and algorithms to enhance the robustness of RAN slicing by addressing the RAN configuration issue for slice recovery in a unified framework, referred to as RS-configuration. This project will build a theoretical and computational scheme that formalizes this insight and provides efficient and practical techniques for RAN slice recovery by mapping VNFs efficiently in the RAN slicing. This research program will also be integrated with education to develop new courses and a new series of case-study class modules in an advanced networking course as well as the design and analysis of algorithm courses at Kennesaw State University. Outreach programs through the university will be utilized to ensure the dissemination of the results. The PI will also release the implementation of the proposed scheme and algorithms in public domains. The goal of this project is to develop optimization models and methods for 1) establishing the theoretical foundation for using RS-configuration to construct a VNF mapping plan for RAN slice recovery optimization; 2) developing a new scheme and algorithms needed to map VNFs efficiently and finally 3) applying our theoretical and algorithmic development to investigate the robustness of RAN slicing and trade-offs between RAN slice recovery and RAN configuration in the RAN-related networking environment. Methodologies will be developed in the context of mathematical programing and graph theory that will help obtain more efficient and fast solutions performing within reasonable bounds of computational complexity. Hence, this research on new scheme and algorithms for RAN slicing will provide the computational basis towards building a robust RAN slicing and contribute to the development of new networking technologies.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.