CAREER: A Platform for Per-Packet AI using Heterogeneous Data Planes

职业：使用异构数据平面的每数据包人工智能平台

• 批准号: 2338034
• 负责人: Muhammad Shahbaz
• 金额: $ 55.54万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-15 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338034&HistoricalAwards=false
• 关键词: CAREER; Platform; Per; Packet; AI

Modern cyberinfrastructure (CI) powers many aspects of our day-to-day life (such as healthcare, finance, electricity, and communication). And, moving forward, our reliance on this infrastructure will grow even more as new use cases (e.g., augmented/virtual reality, autonomous transportation, and remote surgery) and users (e.g., self-driving cars and IoT devices) enter the technological landscape. To meet the strict security and performance requirements of this evolving landscape, the cloud datacenter networks and systems, which form the backbone of CI, must adapt and allocate their (heterogeneous) resources quickly and efficiently. Doing so, however, demands that (compute-intensive) network management and control decisions are applied per packet at line rate in a fast-and-intelligent way. Unfortunately, the dominant solutions available today are neither fast nor intelligent enough to meet these requirements.This proposal aims to bridge this gap between speed and intelligence by developing a holistic platform that allows datacenter operators to execute per-packet AI-driven decisions directly within the network at line rate. The proposal lays out the research across three progressively connected thrusts: (1) designing novel data-plane architectures for per-packet AI, (2) implementing high-level, declarative frameworks for expressing AI objectives (and models), and, finally, (3) developing a suite of per-packet AI applications to build confidence in the utility of the proposed platform. It is a radically new paradigm that converges multiple disciplines (machine learning, networking, and architecture), thus opening pathways for machine-learning researchers and architects—with their cross-disciplinary knowledge—to work alongside network designers to realize the full potential of per-packet AI.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.

现代网络基础设施（CI）为我们日常生活的许多方面（例如医疗保健，金融，电力和沟通）提供了支持。而且，向前迈进，随着新用例（例如，增强/虚拟现实，自动运输和远程手术）以及用户（例如，自动驾驶汽车和物联网设备）进入技术环境，我们对这种基础设施的责任将更加增长。为了满足这种不断发展的景观的严格安全性和性能要求，构成CI骨干的云数据中心网络和系统必须快速有效地适应并分配其（异质）资源。但是，这样做要求（计算密集型）网络管理和控制决策以快速和智能的方式以线路速率应用。不幸的是，当今可用的主要解决方案要么快，要么足够智能满足这些要求。该提案旨在通过开发一个整体平台来弥合速度和情报之间的差距，该平台使数据中心运营商可以按线路直接在网络中直接执行AI-AI-drion驱动决策。该提案列出了三个逐渐连接的推力的研究：（1）设计新颖的数据平面体系结构，用于每包AI，（2）实施用于表达AI目标（和型号）的高级，声明性的框架，以及（最后，（3），（3）为每套AI提供信心，以建立一个构建itsutity Plattertility of the Platferal of Spections of Spections of Spectife ofsuts of Spectssed的提议。这是一个巨大的新范式，它融合了多个学科（机器学习，网络和建筑），因此为机器学习的研究人员和建筑师（凭借其跨学科知识）开辟了途径，可以与网络设计师一起工作，以实现每包AI的全部潜力，以反映了NSF的法定任务和范围的范围，并在宽广的范围内进行了评估。 标准。