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

排除不良网络事件（如连接性或性能差）的故障是非常困难的。局域网上的高速链路技术，如数据包转储和详细流量分析，由于数据量大而无法实现。 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，同时仍然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，该链路流量的大小比原始流量流小几个数量级，但允许与原始数据几乎相同程度的故障排除。 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，需要研究的具体领域包括：确定哪些流构成了故障排除的代表性流，研究如何对采样数据进行最佳编码和解码，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。更广泛的影响：该项目将为本科生提供研究经验。 格鲁吉亚理工学院、丹尼森学院和其他机构的本科生将通过本科生讲习班和研究研讨会招募。 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还将扩大维基百科与数据流算法相关主题的条目，作为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，该项目将导致更好的方法来诊断大规模网络，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网络管理和工程部门的成员合作。

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.