CC* Integration-Small: Integrating Application Agnostic Learning with FABRIC for Enabling Realistic High-Fidelity Traffic Generation and Modeling

网络和应用程序开发的新方法需要逼真的网络使用场景支持的高保真测试和评估。FABRIC测试平台（https：//www.example.com）进一步推动了对这些新方法的追求，能够以当前互联网中不可能的方式在“网络中”存储和处理信息，这将导致全新的网络协议、架构和应用，解决互联网中的性能、安全性和适应性方面的紧迫问题。whatisfabric.net该项目将为研究人员提供一种方法，通过一套新工具轻松利用FABRIC测试平台的新功能-平滑现有实验向测试平台的过渡，并实现令人兴奋的新研究领域。该项目将产生三个系统，促进FABRIC环境中的端到端流量建模和生成。将创建一个模型库，供实验人员存储和访问自定义模型，并将一些流行应用程序的库存模型作为种子供立即使用。在FABRIC主持的实验中使用模型将通过一个定制的匹配系统来推进，该系统将使实验资源与模型要求相一致。最后，对于开发新应用程序的实验，将提供一个工具，用于使用FABRIC基础设施组件捕获的数据创建新模型。FABRIC用户将获得直接低摩擦访问测试平台的新基础设施功能，使他们能够将大部分时间和精力集中在自己的研究目标上，而不是处理资源可用性，专业驱动程序设置和复杂数据格式的变幻莫测。 因此，实验台资源可以在实验之间更优化地共享，并且单个研究任务将更快地完成。该项目还将为未来的研究人员和测试平台实施者提供输入，以简化高级服务的工作流程，支持先进测试平台技术的研究目标。项目工具和代码的文档以及支持项目数据将位于http://docs.uh-netlab.org，并且将在实质性项目工作结束后至少5年内公开。 开发中的源代码可通过文档网站链接的公共互联网资源持续获得。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Novel approaches to networking and application development require high-fidelity testing and evaluation supported by realistic network usage scenarios. Furthering the pursuit of these novel approaches, the FABRIC testbed (https://whatisfabric.net) can store and process information "in the network" in ways not possible in the current Internet, which will lead to completely new networking protocols, architectures and applications that address pressing problems with performance, security and adaptability in the Internet. This project will provide researchers the means to easily utilize the new capabilities of the FABRIC testbed through a suite of new tools - smoothing the transition of existing experiments to the testbed and enabling exciting new areas of research.This project will produce three systems facilitating end to end traffic modeling and generation in the FABRIC environment. A model repository will be created for the storage and access of custom models by experimenters, and will be seeded with stock models of some popular applications for immediate use. The use of the models within FABRIC-hosted experiments will be advanced through a bespoke matching system that will align experiment resources with model requirements. Finally, for experiments developing novel applications, a tool will be provided for creating new models using data captured with FABRIC infrastructure components. FABRIC users will gain direct low-friction access to the novel infrastructure capabilities of the testbed, freeing them to focus the bulk of their time and effort on their own research goals rather than dealing with the vagaries of resource availability, specialized driver setup, and complex data formats. As a result, testbed resources can be more optimally shared between experiments, and individual research tasks will be completed more quickly. The project will also provide input to future researchers and testbed implementors on streamlining workflows of high level services in support of research objectives over advanced testbed technologies.Documentation for project tools and code, as well as backing project data, will be located at http://docs.uh-netlab.org, and it will be publicly available for at least 5 years after the end of substantive project work. In-development source code is available on an ongoing basis via public internet resources linked from the documentation site.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.