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

对新的计算机网络应用程序和基础设施的行业评估以及对建议的平台和协议的研究评估依赖于对实际网络使用情况的高质量表示。从历史上看，这些需求主要是通过使用依赖于通用流量模型的工作负载生成器来满足的。然而，随着应用的快速发展、移动性的扩大以及物联网(IoT)设备在最终用户网络中日益普及，这些模式越来越被证明不适用于现代环境。因此，在生产部署之前，新的方法、协议和硬件很难在真实场景中进行验证。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实际模型。通过该项目开发的工具使用了将特定于应用程序的行为与特定于基础设施的行为分离的新型专家系统和捕获复杂应用程序交换的机器学习(ML)管道的组合，以便为现有生成器提供实际的应用程序流量模式模型。标签选择、分类器设计和现有ML算法的选择将推动关于未来研究方向以及当前工具和模型最佳使用环境的文档。作为该项目的结果，应用流量模式的抽象但高保真模型的生成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，可显著提高可用于规划和研究任务的流量模型的质量，同时还可为探索新协议和基础设施设计的环境提供可移植性。这些模型的可获得性使得前瞻性研究的有效性和重复性大大提高。此外，这些模型的制作将与高中、本科和研究生级别的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，具有代表性的拓扑和应用程序流量推动实际操作实验室。项目工具和代码的文档以及支持项目数据的文档将位于http://docs.uh-netlab.org/appmodel/index.html，，并将在实质性项目工作结束后至少5年内公开提供。源代码(连同文档源)将在比特桶(http://www.bitbucket.org/uh-netlab/))上持续提供。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.