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高保真测试和评估，并得到现实网络使用场景的支持。为了进一步推动对这些新方法的追求，Fabric TestBed(https://whatisfabric.net))可以以当前互联网无法实现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、体系结构和应用程序，以解决互联网中的性能、安全和适应性方面的紧迫问题。该项目将为研究人员提供通过一套新工具轻松利用织物试验台的新功能的方法--平滑现有实验到试验台的过渡，并支持令人兴奋的新研究领域。该项目将产生三个系统，促进在织物环境中进行端到端流量建模和生成。将创建一个模型库，供实验者存储和访问定制模型，并将一些流行应用的库存模型播种到该库中，以供立即使用。在织物托管的实验中，模型的使用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保持一致。最后，对于开发新应用程序的实验，将提供一个工具，用于使用通过交换矩阵基础设施组件捕获的数据创建新模型。Fabric用户将直接低摩擦地访问试验台的新型基础设施功能，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，而不是处理变幻莫测的资源可用性、专门的驱动程序设置和复杂的数据格式。因此，实验台资源可以在实验之间得到更优化的共享，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试验台实施者提供关于简化高级服务的工作流程的意见，以支持先进的试验台技术的研究目标。项目工具和代码的文件以及支持项目数据的文件将位于http://docs.uh-netlab.org，，并将在实质性项目工作结束后至少5年内公开提供。开发中的源代码可以通过文档站点链接的公共互联网资源持续提供。该奖项反映了NSF的法定使命，并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。

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.