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

排除不希望出现的网络事件（例如连接差或性能差）是非常困难的。由于数据量巨大，在局域网中使用的高速链路技术（如数据包转储和详细流量分析）是不可能实现的。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其网络进行故障排除。该项目的目标是从单个网络链路上的高速数据包流中提取链路流量的近似和高度压缩表示，其大小比原始流量流小几个数量级，但它允许几乎相同程度的故障排除作为原始数据。该项目将开发设计用于压缩网络流量的智能采样算法所需的算法和数学理论。为了开发采样技术的目的，需要研究的特定领域包括确定构成代表性流的内容以进行故障排除，研究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本，以便在新数据到达时回收空间。广泛影响：该项目将为本科生提供研究经验。佐治亚理工学院、丹尼森学院和其他院校的本科生将通过本科生研讨会和研究研讨会招聘。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还将扩展与数据流算法相关主题的维基百科条目，作为向科学界传播主题领域一般信息过程的一部分。就商业影响而言，该项目将为大规模网络的诊断提供更好的方法，从而降低维护和运营成本。作为将研究成果转化为商业实践的一部分，pi将与at&t网络管理和工程部的成员合作。

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.