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

CC* Integration-Small：将应用程序无关学习与 FABRIC 集成，以实现现实的高保真流量生成和建模

• 批准号: 2419070
• 负责人: Deniz Gurkan
• 金额: $ 30万
• 依托单位: Kent State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2419070&HistoricalAwards=false
• 关键词: CC; Integration; Small; Integrating; Application

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.

新颖的网络和应用程序开发方法需要由现实网络使用场景支持的高保真测试和评估。为了进一步追求这些新颖的方法，FABRIC 测试平台 (https://whatisfabric.net) 可以以当前互联网不可能的方式在“网络中”存储和处理信息，这将带来全新的网络协议、架构和解决互联网中性能、安全性和适应性等紧迫问题的应用程序。该项目将为研究人员提供通过一套新工具轻松利用 FABRIC 测试台新功能的方法 - 使现有实验顺利过渡到测试台并实现令人兴奋的新研究领域。该项目将产生三个系统，促进端到端最终在 FABRIC 环境中进行流量建模和生成。将创建一个模型存储库，用于实验者存储和访问自定义模型，并将植入一些流行应用程序的库存模型以供立即使用。 FABRIC 托管实验中模型的使用将通过定制匹配系统来推进，该系统将使实验资源与模型要求保持一致。最后，对于开发新颖应用程序的实验，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 用户将能够直接、轻松地访问测试台的新颖基础设施功能，从而使他们能够将大量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问题。数据格式。 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台实施者提供关于简化高级服务工作流程的投入，以支持先进测试台技术的研究目标。项目工具和代码以及支持项目数据的文档将位于http:// /docs.uh-netlab.org，并将在实质性项目工作结束后至少公开 5 年。 正在开发的源代码可通过文档网站链接的公共互联网资源持续获取。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。