NeTS: Small: Collaborative Research: Towards Principled Network Troubleshooting via Efficient Packet Stream Processing

NetS：小型：协作研究：通过高效的数据包流处理实现有原则的网络故障排除

• 批准号: 1218092
• 负责人: Jun Xu
• 金额: $ 30.6万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218092&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Towards

Troubleshooting undesirable network events, such as poor connectivity or performance, is difficult at best. The high-speed link techniques that work on LANs, such as dumping packets and analyzing the detailed traffic, are impossible due to massive data volume. This project will explore mathematical techniques and network tools that will reduce the amount of data that has to be captured and stored while still allowing network operators to troubleshoot their networks. The project's objective is to extract from high-speed packet streams on individual network links an approximate and highly compressed representation of the link traffic that is orders of magnitude smaller in size than the raw traffic stream but which permits almost the same degree of troubleshooting as the raw data. The project will develop the algorithms and mathematical theory needed to design intelligent sampling algorithms for compressing network traffic. Specific areas to be studied for purposes of developing sampling techniques include identifying what constitutes the representative flows for troubleshooting purposes and investigating how to best encode and decode the sampled data, and how the samples can be gracefully shrunk over time so as to reclaim space for new data as they arrive.Broader Impact:The project will provide research experience for undergraduates. Undergraduates at Georgia Tech, Denison and other institutions will be recruited via undergraduate workshops and research symposiums. Additionally, the project will integrate education and research via inclusion of the research into courses. In addition to publishing in appropriate scientific venues, the PIs will expand the Wikipedia entries on topics related to data streaming algorithms as part of the process of disseminating general information about the topic area to the scientific community. In terms of commercial impact, the project will lead to better methods for the diagnosis of large-scale networks, thereby reducing the cost to maintain and operate them. As part of the transfer of research findings into commercial practice the PIs will collaborate with members of AT&T's Network Management and Engineering Department.

对不良网络事件进行故障排除，例如连通性或性能差，充其量很难。由于大量的数据量，不可能在LAN上使用的高速链路技术，例如倾倒数据包和分析详细的流量。 该项目将探索数学技术和网络工具，以减少必须捕获和存储的数据量，同时仍允许网络运营商对网络进行故障排除。 该项目的目标是从单个网络链接上的高速数据包流中提取链接流量的近似且高度压缩的表示，该链路流量的大小比原始流量流小的数量级，但几乎允许与原始数据相同的故障排除。 该项目将开发设计智能采样算法以压缩网络流量所需的算法和数学理论。 出于开发采样技术的目的，要研究的特定领域包括确定用于故障排除目的的代表性流程的构成的内容，并调查如何最好地编码和解码采样数据，以及如何随着时间的推移优雅地缩小样品，以便如何在到达时为新数据收回空间。 佐治亚理工学院，丹尼森和其他机构的本科生将通过本科研讨会和研究研讨会招募。 此外，该项目将通过将研究纳入课程来整合教育和研究。 除了在适当的科学场所发布外，PI还将扩大与数据流算法有关的主题的Wikipedia条目，这是向科学界传播有关主题领域的一般信息的过程的一部分。 在商业影响方面，该项目将为诊断大规模网络的诊断提供更好的方法，从而降低维护和操作它们的成本。 作为将研究结果转移到商业实践的一部分，PI将与AT＆T网络管理和工程部门的成员合作。