NeTS: Small: Understanding, Managing and Trouble-Shooting the Evolving Cellular Data Networks

NeTS：小型：了解、管理和排除不断发展的蜂窝数据网络的故障

• 批准号: 1117536
• 负责人: Zhi-Li Zhang
• 金额: $ 30万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117536&HistoricalAwards=false
• 关键词: NeTS; Small; Understanding; Managing; Trouble

With the wide adoption of smart phones and other mobile devices, cellular mobile data traffic has grown tremendously in the past few years. This rapid growth in cellular data traffic, coupled with the increasing demands for mobile data services, has exerted enormous competitive pressure on cellular network service providers. How to effectively manage a large-scale cellular data network is therefore a daunting challenge that is not only important to cellular network service providers, but also to mobile data users and emerging mobile applications and services. In this project, the PI sets out to develop a data-driven, statistical machine learning-based framework to identify, analyze and understand various challenging issues in cellular data networks, and develop mechanisms and tools for effectively managing and trouble-shooting constantly evolving cellular data networks. The PI leverages vast amounts of heterogeneous sources of data obtained from operational cellular data networks, and designs statistical inference techniques to analyze, mine and correlate various sources of real-world data to uncover and identify various key factors that contribute to network performance issues and affect user experiences.This research project brings benefits not only to cellular service providers, but also to millions of users who are increasingly dependent on mobile voice and data services. It also provides valuable insights to inform further development of emerging mobile and cloud computing. Research outcomes will be disseminated through publications and outreach activities, as well as technology transfer.

随着智能手机和其他移动设备的广泛采用，蜂窝移动数据流量在过去几年中大幅增长。蜂窝数据流量的快速增长，加上对移动数据服务的需求不断增长，给蜂窝网络服务提供商带来了巨大的竞争压力。因此，如何有效地管理大规模蜂窝数据网络是一项艰巨的挑战，不仅对蜂窝网络服务提供商很重要，而且对移动数据用户以及新兴的移动应用和服务也很重要。在该项目中，PI着手开发一个数据驱动的、基于统计机器学习的框架，以识别、分析和理解蜂窝数据网络中的各种具有挑战性的问题，并开发用于有效管理和排除不断发展的蜂窝数据网络故障的机制和工具。 PI利用从运营的蜂窝数据网络获得的大量异构数据源，并设计统计推断技术来分析、挖掘和关联各种真实世界数据源，以发现和识别导致网络性能问题和影响用户体验的各种关键因素。该研究项目不仅为蜂窝服务提供商带来好处，也为数百万日益依赖移动语音和数据服务的用户带来好处。它还提供了宝贵的见解，为新兴移动和云计算的进一步发展提供信息。研究成果将通过出版物和外展活动以及技术转让来传播。