CC* Integration-Small: Enhancing Data Transfers by Enabling Programmability and Closed-loop Control in a Non-programmable Science DMZ

可编程数据平面（PDP）交换机最近吸引了通信行业的极大关注。它们是网络设备，提供了数据网络中发生的事件的前所未有的可见性，并使工程师能够使用P4编程语言编写软件应用程序。应用程序利用PDP交换机的可见性和性能容量，其中应用程序的运行速度比在通用计算机上运行的应用程序快几个数量级。该项目将在南卡罗来纳州（USC）部署一个“自动驾驶”网络--称为科学DMZ --使用在PDP交换机上运行的P4应用程序。加强后的网络将支持促进科学进步的科学和工程项目。具体来说，它将使教职员工、研究人员和学生能够在南加州大学校园内更有效地传输大科学数据，并与美国能源部实验室等外部合作者（例如，萨凡纳河、阿贡）、国家计算中心（例如，圣地亚哥超级计算机中心、国家能源研究科学计算中心）和国际组织（例如，欧洲核子研究组织（CERN）。该项目有两个目标：1）开发一个自动驾驶的科学DMZ，能够提高性能和公平性。流量将由以每秒太比特（Tbps）速率运行的PDP交换机监控，通常在通用CPU上执行的功能将卸载到PDP交换机。该计划将利用粒度遥测和交换机的处理速度来自动配置科学DMZ，并解决南加州大学和社区观察到的挑战，例如流量监管和最佳缓冲区大小。此外，该项目将扩展perfSONAR以被动地纳入PDP交换机生成的遥测，引入基于每个流的实时报告，并添加队列占用率和数据包到达间隔时间等新指标（即，每分组可见性）。2)通过开发开源P4虚拟实验室库来传播P4技术。这些图书馆将在南加州大学的云平台上运行，称为学术云，并在FABRIC上运行。学术云被几所学院和大学、研究人员和网络基础设施（CI）社区用于学术课程、研讨会和自定进度的培训。FABRIC上的库将使用MysterHub笔记本，结合学习者的定制拓扑，并将P4代码嵌入解释性文档，视频和其他视觉辅助工具。最后，该项目将支持四名博士生和十名本科生，他们将在首席研究员和CI工程师的指导下部署增强的网络。该项目由高级网络基础设施办公室，刺激竞争研究的既定计划（EPSCoR），该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

Programmable data plane (PDP) switches have recently attracted significant attention from the communications industry. They are network devices that provide unprecedented visibility of events occurring in data networks and enable engineers to write software applications using the P4 programming language. The applications leverage the visibility and performance capacity of PDP switches, where applications can run orders of magnitude faster than those running on general-purpose computers. This project will deploy a "self-driving" network – referred to as Science DMZ – at the University of South Carolina (USC), using P4 applications running on PDP switches. The enhanced network will support science and engineering projects that foster the progress of science. Specifically, it will enable faculty members, researchers, and students to transfer big science data more efficiently within USC's campus and with external collaborators such as U.S. Department of Energy's laboratories (e.g., Savannah River, Argonne), national computing centers (e.g., San Diego Supercomputer Center, National Energy Research Scientific Computing Center), and international organizations (e.g., European Organization for Nuclear Research – CERN). The project has two objectives: 1) Develop a self-driving Science DMZ, capable of enhancing performance and fairness. Traffic will be monitored by PDP switches operating at terabits per second (Tbps) rate, and functions commonly executed on general-purpose CPUs will be offloaded to the PDP switches. The scheme will leverage the granular telemetry and the processing speed of the switches to automate the configuration of the Science DMZ and address challenges observed by USC and the community, such as traffic policing and optimal buffer sizing. Additionally, the project will extend perfSONAR to passively incorporate the telemetry generated by PDP switches, introduce real-time reporting on a per-flow basis, and add new metrics such as queue occupancy and packet interarrival time (i.e., per-packet visibility). 2) Disseminate the P4 technology by developing open-source P4 virtual lab libraries. The libraries will run on USC's cloud platform, referred to as the Academic Cloud, and on FABRIC. The Academic Cloud is used by several colleges and universities, researchers, and the cyberinfrastructure (CI) community for academic courses, workshops, and self-paced training. The libraries on FABRIC will use JupyterHub notebooks, incorporate the learner's customized topology, and embed P4 code alongside explanatory documentation, videos, and other visual aids. Finally, the project will support four PhD students and ten undergraduate students who will deploy the enhanced network under the guidance of the Principal Investigators and CI engineers.This project is jointly funded by the Office of Advanced Cyberinfrastructure, the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Computer and Network Systems.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.