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CNS Core: Small: Realistic Traffic Generation through Application-Agnostic Learning

CNS 核心：小型：通过与应用无关的学习生成真实流量

基本信息

批准号：
1908974
负责人：
Deniz Gurkan
金额：
$ 34.58万
依托单位：
University of Houston
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908974&HistoricalAwards=false
关键词：
CNS Core Small Realistic Traffic

项目摘要

Industry evaluation of new computer network applications and infrastructure - as well as research evaluation of proposed platforms and protocols - rely on high quality representations of realistic network usage. Historically these needs have largely been met through the use of workload generators that rely on generalized traffic models. However, these models have increasingly proven inadequate in modern environments with fast-evolving applications, expanding mobility, and the rising prevalence of Internet-of-Things (IoT) devices in end-user networks. As a result, novel methods, protocols, and hardware prove difficult to verify in real-world scenarios prior to production deployment. This project distills complex applications into realistic models that can be used to evaluate future systems and deployments.The tools developed through this project employ a combination of a novel expert system that separates application-specific behavior from infrastructure-specific behavior and a machine learning (ML) pipeline that captures complex application exchanges in order to provide realistic models of application traffic patterns for use in existing generators. Label selection, classifier design, and choices of existing ML algorithms will drive documentation on both future research direction as well as the environments in which current tools and models are best employed.The generation of abstract but high-fidelity models of application traffic patterns as a result of this project provides valuable data to industry users and researchers alike. The separation of application behavior from potentially sensitive and proprietary input data allows for significant expansion of the quality of traffic models available for planning and research tasks while also providing portability to environments exploring new protocols and infrastructure design. The availability of these models allows for significantly more effective validation and reproducibility of prospective studies. Furthermore, the production of these models will integrate with educational outreach efforts to high school, undergraduate, and graduate level courses on computer networking and cybersecurity where representative topologies and application traffic drive hand-on labs.Documentation for project tools and code, as well as backing project data, will be located at http://docs.uh-netlab.org/appmodel/index.html, and it will be publicly available for at least 5 years after the end of substantive project work. Source code (along with documentation source) will be made available at bitbucket (http://www.bitbucket.org/uh-netlab/) on an ongoing basis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

新的计算机网络应用和基础设施的行业评估-以及建议的平台和协议的研究评估-依赖于现实网络使用的高质量表示。从历史上看，这些需求在很大程度上是通过使用依赖于广义流量模型的工作负载生成器来满足的。然而，这些模型越来越多地被证明在现代环境中是不够的，这些环境具有快速发展的应用程序，不断扩大的移动性，以及物联网（IoT）设备在最终用户网络中的日益普及。因此，新的方法、协议和硬件在生产部署之前很难在现实世界的场景中进行验证。该项目将复杂的应用程序提炼成可用于评估未来系统和部署的现实模型。通过该项目开发的工具采用了一种新型专家系统和机器学习（ML）管道的组合，该专家系统将特定于应用程序的行为与特定于基础设施的行为分离开来，该机器学习管道捕获复杂的应用程序交换，以便提供应用程序流量模式的现实模型，供现有生成器使用。标签选择、分类器设计和现有ML算法的选择将推动未来研究方向以及当前工具和模型最佳使用环境的文档化。该项目产生的抽象但高保真的应用流量模式模型为行业用户和研究人员提供了有价值的数据。将应用程序行为与潜在的敏感和专有输入数据分离，可以显著扩展可用于规划和研究任务的流量模型的质量，同时还为探索新协议和基础设施设计的环境提供可移植性。这些模型的可用性使得前瞻性研究的验证和再现性显著提高。此外，这些模型的制作将与高中、本科和研究生计算机网络和网络安全课程的教育推广工作相结合，在这些课程中，代表性的拓扑结构和应用程序流量将推动动手实验。项目工具和代码的文档以及支持项目的数据将位于http：//docs.uh-netlab.org/appmodel/index.html，并且将在实质性项目工作结束后至少5年内公开。源代码（沿着文档源）将在bitbucket（http：//www.bitbucket.org/uh-netlab/）上持续提供。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。