Between Packets and Predictions: Analyzing the Overhead of In-Network Machine Learning in Programmable Switches - Silva, Caio Luiz L. T.; Silva, Icaro M. da; Gouveia, Thiago; Almeida, Leandro C. de (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2026)
Recent advances in programmable data planes have sparked significant interest in executing machine learning inference directly within network devices. Prior work demonstrates the feasibility of this paradigm across diverse applications, ranging from Network Security to Quality of Service. However, the network-level performance overhead and effects in perceived quality remain largely unexplored. This paper assesses the impact of executing machine learning inference inside network devices on key metrics such as throughput, queueing delay, and application quality. Through controlled experiments covering web traffic, bulk file transfer, and video streaming, the performance of machine-learning-enabled pipelines is contrasted with standard forwarding. Specific attention is given to adaptive video delivery, where MPEG-DASH metrics indicate that while adaptive mechanisms preserve playback stability, the integration of additional processing features leads to a saturation of quality gains and a marked increase in tail latency. The findings provide valuable insights into the systemic costs of embedding intelligence into smartswitch data planes.