Programmable Data Planes (P4, eBPF) for High-Performance Networking: Architectures and Optimizations for AI/ML Workloads

Abstract

Artificial intelligence (AI) and machine learning (ML) workloads are getting more complex and latency-sensitive, with traditional network infrastructures becoming less and less suitable in meeting the new requirements of high-throughput/low-latency data flow. With the new technologies that are making programmable data planes a reality (eBPF, P4), performance, flexibility, and observability are being pushed to new limits in high-speed networks. In contrast to fixed-function pipelines, customization of packet processing, telemetry, flow control and security enforcement can be customized in real-time within the programmable data plane at the network edge, or more directly in the data center fabric.
The current paper will discuss the use of P4 and eBPF in enhancing AI/ML traffic patterns and the ability to create dynamic network behaviors and how these approaches facilitate the scalability of infrastructure in cloud and edge computing systems. We analyze fundamental architecture concepts, execution structures, and designated designs that aid intelligent load balancing, granular QoS and adaptive traffic rerouting. By doing comparative analysis, performance benchmarking and real-life use-case studies we show the practical effect of programmable data planes with respect to AI/HPC-driven infrastructure. Both of our results emphasize not only a substantial increase in throughput and responsiveness but also the rise of the software-defined networking (SDN) frameworks to suit AI-centric data streams.