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

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.