Resource Management in Cloud Radio Networks

近年来，移动设备上的内容创作和消费的爆炸式增长导致了移动数据流量的大幅增长。为了满足日益增长的用户需求，移动网络运营商正日益朝着基站密集部署的方向发展。然而，部署大量基站会导致网络的资本和运营成本显著增加。云无线接入网（CRAN）是一种新兴的移动网络架构，其中信号处理功能转移到数据中心，将基站转变为简单的低成本远程无线电单元。基于云的架构不仅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，这些模块可以动态扩展以适应不同的用户需求，从而提高网络的可扩展性和性能。虽然CRAN在概念上很简单，但在实现之前需要解决一些技术和智力上的挑战。一个关键的挑战是高效和有效地管理交织在一起的无线电（例如，无线电频率和发射功率）和数据中心（例如，服务器和互连链路）资源。我们的研究观点是，即使每个资源都基于最先进的技术进行管理，对这些资源的单独管理也不是最佳的。本项目旨在统一介绍和研究CRAN中最优或接近最优的资源管理算法。我们在研究中追求的一个具有直接实际意义的主要概念是主动资源管理，它保证我们的算法在动态需求下表现良好，而不需要昂贵和破坏性的重新配置。我们的目标是通过开发框架来研究：i)在线资源管理，当没有关于未来需求的信息可用时，ii)鲁棒资源管理，当只有关于未来需求的部分信息可用时，iii)自主资源管理，当自主学习最佳资源管理算法时，为云无线接入网络中的主动资源管理做出基础贡献。我们专注于CRAN，考虑到未来的移动技术，但要构建和研究的基本工具和方法也与其他基于云的系统相关。提出的研究将为以云为中心的移动网络中的资源管理产生新的算法和理论框架。它将为其他研究人员提供一个创新的框架来设计自主资源管理算法，以及一套基于传统优化技术的高效算法，其性能特征和权衡是很好的量化。加拿大移动运营商和云服务提供商在规划新服务和应用程序时，将能够利用我们的研究结果更好地为他们的决策提供信息。

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.