NeTS: Small: Collaborative Research: Understanding Traffic Dynamics in Cellular Data Networks and Applications to Resource Management

宽带蜂窝网络正在成为全世界移动的数据访问的最常见手段。行业分析师的预测表明，通过蜂窝数据网络的数据量将在不久的将来呈指数级增长。通过测量和分析来理解移动的数据业务对于这些网络的资源管理技术的发展是至关重要的。虽然频谱资源是非常令人关注的，但该项目特别关注运营蜂窝网络基础设施（特别是基站）所需的能源。该项目进行了一个重要的建模练习，有两个目标。一个目标是智力，朝着理解时空动态的移动的交通和发现可能的结构或关系驱动。该项目使用最先进的机器学习工具，利用直接从运营商网络收集的大规模数据开发模型。这种建模将带来新的见解，反过来将有助于部署和管理下一代蜂窝数据网络。第二个目标是功利性的。在此，开发了用于预测基站负载以用于资源管理（特别是能量）的技术。算法的目的是利用能源优化的机会，关闭特定的网络资源的基础上预测的负载。该项目具有显着的更广泛的影响。它开发技术，以显著降低蜂窝网络的能源消耗。总的来说，这一做法既能降低成本，又有利于环境。该项目还为这两个机构的若干绿色举措以及研究生的教育和培训做出了贡献。

Broadband cellular networks are emerging to be the most common means for mobile data access worldwide. Predictions from industry analysts indicate that the volume of data through cellular data networks will increase exponentially in near future. Understanding of the mobile data traffic via measurement and analysis is critical for the development of resource management techniques for these networks. While spectrum resources are of great concern, this project specifically focuses on the energy required to operate the cellular network infrastructure, specifically base stations. The project undertakes a significant modeling exercise with two goals. One goal is intellectual, driven towards understanding the spatio-temporal dynamics of mobile traffic and discovering possible structure or relationships. The project uses state-of-the-art machine learning tools to develop models using large-scale data collected directly from the operator's networks. Such modeling will bring new insights that in turn will help to deploy and manage future generation cellular data networks. The second goal is utilitarian. Here, techniques are developed to predict base station loads for use in resource management, specifically energy. Algorithms are designed to exploit energy-optimization opportunities to turn off specific network resources based on the forecasted load.The project has significant broader impact. It develops technologies to appreciably reduce energy consumption in cellular networks. Overall, this exercise will both reduce cost, and contribute to the environment. The project also contributes to several green initiatives in both institutions and to the education and training of graduate students.