DUMBO accepted at Conext 2024

Our paper titled “Taming the Elephants: Affordable Flow Length Prediction inthe Data Plane” will be presented at Conext 2024

Congrats to the team, especially Raphael, Andrea, and Gabriele.

Abstract: Machine Learning (ML) shows promising potential for enhancing networking tasks. In particular, early flow size prediction would be beneficial for a wide range of use cases. However, implementing an ML-enabled system is a challenging task due to network devices limited resources. Previous works have demonstrated the feasibility of running simple ML models in the data plane, yet their integration in a practical end-to-end system is not trivial. Additional challenges in resources management and model maintenance need to be addressed to ensure the network task(s) performance improvement justifies the system overhead. In this work, we propose DUMBO, a versatile end-to-end system to generate and exploit flow size hints at line rate.Our system seamlessly integrates and maintains a simple ML model that offers early coarse-grain flow size prediction in the data plane. We evaluate the proposed system on flow scheduling, per-flow packet inter-arrival time distribution, and flow size estimation using real traffic traces, and perform experiments using an FPGA prototype running on an AMD(R)-Xilinx(R) Alveo U280 SmartNIC. Our results show that DUMBO outperforms traditional state-of-the-art approaches by equipping network devices data planes with a lightweight ML model.