Resource Management in Cloud Radio Networks

云无线电网络中的资源管理

• 批准号: RGPIN-2019-04819
• 负责人: Ghaderi, Majid
• 金额: $ 2.48万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737880
• 关键词: Resource; Management; Cloud; Radio; Networks

The explosive growth in creation and consumption of content on mobile devices has led to a massive increase in mobile data traffic in recent years. In order to satisfy growing user demands, mobile network operators are increasingly moving toward denser deployment of base stations. However, deploying a large number of base stations results in significant increases in capital and operational costs of the network. Cloud radio access network (CRAN) is an emerging mobile network architecture in which signal processing functions are moved to a datacenter, turning base stations into simple low-cost remote radio units. Not only the cloud-based architecture reduces the cost and complexity of deploying more base stations, but also allows signal processing functions to be virtualized in software modules that can be dynamically scaled to adapt to varying user demands, improving network scalability and performance. While CRAN is conceptually simple, several technological and intellectual challenges need to be addressed before it can be realized. A key challenge is the efficient and effective management of intertwined radio (e.g., radio frequency and transmit power) and datacenter (e.g., servers and interconnection links) resources. Our research view is that separate management of these resources is not optimal, even if each one is managed based on state-of-the-art techniques. The goal of this Discovery Program is to introduce and study, in a unified way, optimal or close to optimal algorithms for resource management in CRAN. One of the major concepts we pursue in our research, which has direct practical implications, is proactive resource management, where it is guaranteed that our algorithms perform well under dynamic demands without requiring costly and disruptive reconfigurations. We aim to make foundational contributions toward proactive resource management in cloud radio access networks by developing frameworks to study: i) online resource management, when no information about future demands is available, ii) robust resource management, when only partial information about future demands is available, and iii) autonomic resource management, when the optimal resource management algorithm is learnt autonomously. We focus on CRAN, having future mobile technologies in mind, but the basic tools and approaches to be built and researched are relevant to other cloud-based systems as well. The proposed research will produce new algorithms and theoretical frameworks for resource management in cloud-centric mobile networks. It will provide other researchers with an innovative framework to design autonomic resource management algorithms as well as a suite of efficient algorithms based on conventional optimization techniques, whose performance characteristics and trade-offs are well quantified. Canadian mobile operators and cloud service providers will be able to use our results to better inform their decisions when planning new services and applications.

移动设备上内容的创建和内容消耗的爆炸性增长导致近年来移动数据流量的大量增加。为了满足不断增长的用户需求，移动网络运营商越来越多地走向基本站的密集部署。但是，部署大量基站会导致网络的资本和运营成本显着增加。云无线电访问网络（CRAN）是一种新兴的移动网络体系结构，其中信号处理功能移至数据中心，将基站转换为简单的低成本远程无线电单元。不仅基于云的体系结构降低了部署更多基站的成本和复杂性，而且还允许在软件模块中虚拟化信号处理功能，这些软件模块可以动态缩放以适应不同的用户需求，从而提高网络可扩展性和性能。 尽管Cran在概念上很简单，但在实现它之前需要解决一些技术和智力挑战。一个关键的挑战是相互交织的无线电（例如，无线电频率和传输功率）和数据中心（例如服务器和互连链接）资源的有效管理。我们的研究观点是，即使每个资源都基于最先进的技术对每个资源进行管理也不是最佳的。该发现计划的目的是以统一的方式介绍和研究Cran中资源管理的最佳或接近最佳算法。我们在研究中追求的主要概念之一，具有直接的实际含义，是积极主动的资源管理，可以保证我们的算法在动态的需求下表现良好，而无需需要代价高昂和颠覆性的重新配置。我们旨在通过开发框架来研究云无线电访问网络中积极的资源管理基础贡献：i）在线资源管理，当没有有关未来需求的信息时，ii）ii）强大的资源管理，当仅提供有关未来需求的部分信息； iiii）自主资源管理，当最佳资源管理Algorithm是自动学习的。我们专注于Cran，考虑未来的移动技术，但是要构建和研究的基本工具和方法也与其他基于云的系统有关。拟议的研究将在以云为中心的移动网络中生成用于资源管理的新算法和理论框架。它将为其他研究人员提供创新的框架，以设计自主性资源管理算法以及基于常规优化技术的一系列有效算法，其性能特征和权衡得到很好的量化。加拿大移动运营商和云服务提供商将能够使用我们的结果在计划新服务和应用程序时更好地告知他们的决策。