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.

网络和应用程序开发的新方法需要由实际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Internet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、体系结构和应用程序，以解决Internet中性能、安全性和适应性方面的紧迫问题。该项目将通过一套新工具为研究人员提供方便地利用FABRIC试验台新功能的方法——平滑现有实验到试验台的过渡，并使令人兴奋的新研究领域成为可能。该项目将产生三个系统，在FABRIC环境中促进端到端流量建模和生成。实验人员将创建一个模型存储库，用于存储和访问自定义模型，并将使用一些流行应用程序的库存模型作为种子，以便立即使用。在fabric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需求保持一致。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FABRIC基础设施组件捕获的数据创建新模型。FABRIC用户将获得对试验台新颖的基础设施功能的直接低摩擦访问，使他们能够将大量时间和精力集中在自己的研究目标上，而不是处理资源可用性的变化、专门的驱动程序设置和复杂的数据格式。因此，试验台资源可以在实验之间更优化地共享，个人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，以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，并将在实质性项目工作结束后至少5年内向公众开放。开发中的源代码可以通过从文档站点链接的公共internet资源持续获取。该奖项反映了美国国家科学基金会的法定使命，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。