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球移动数据接入最常见的手段。行业分析师的预测表明，在不久的将来，通过蜂窝数据网络的数据量将呈指数级增长。通过测量和分析了解移动数据流量对于开发这些网络的资源管理技术至关重要。虽然频谱资源非常令人担忧，但该项目特别关注运营蜂窝网络基础设施所需的能源，特别是基站。该项目承担了一项重要的建模工作，目标有两个。一个目标是智力，推动理解移动流量的时空动态，并发现可能的结构或关系。该项目使用最先进的机器学习工具，使用直接从运营商网络收集的大规模数据来开发模型。这样的建模将带来新的见解，进而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。这里，开发了预测基站负载的技术，以用于资源管理，特别是能量管理。算法旨在利用能量优化机会，根据预测的负载关闭特定的网络资源。该项目具有重大的更广泛的影响。它开发的技术可以显著降低蜂窝网络的能源消耗。总体而言，这项工作既可降低成本，又可对环境作出贡献。该项目还为这两个机构的若干绿色倡议以及研究生的教育和培训作出了贡献。

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.