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

现代网络基础设施（CI）为我们日常生活的许多方面（如医疗保健，金融，电力和通信）提供动力。而且，随着新的用例（例如，增强/虚拟现实、自主运输和远程手术）和用户（例如，自动驾驶汽车和物联网设备）进入技术领域。为了满足这一不断发展的环境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和分配其（异构）资源。然而，这样做要求以快速和智能的方式以线路速率对每个数据包应用（计算密集型）网络管理和控制决策。不幸的是，目前主流的解决方案既不快速，也不智能，无法满足这些要求。该提案旨在通过开发一个整体平台，让数据中心运营商直接在网络中以线速执行每个数据包的AI驱动决策，从而弥合速度和智能之间的差距。该提案提出了三个逐步连接的研究方向：（1）为每包AI设计新的数据平面架构，（2）实现用于表达AI目标（和模型）的高级声明性框架，以及（3）开发一套每包AI应用程序，以建立对所提出平台实用性的信心。这是一个全新的范式，融合了多个学科（机器学习，网络和架构），从而为机器学习研究人员和架构师开辟了道路-凭借他们的跨学科知识-与网络设计师一起工作，以实现每个人的全部潜力。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的支持影响审查标准。

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.