Hierarchical Offloading Framework for ML-Driven Network Functions Across Heterogeneous Data Planes

While Pekár et al. and Moro et al. discuss offloading complex functions from P4 switches to NFV infrastructure, and Parizotto et al. survey ML offloading, current approaches typically use static offloading policies or focus on either hardware or software execution. This idea proposes a hierarchical, ML-aware scheduler that partitions network function logic (e.g., intrusion detection, load balancing, QoS enforcement) across hardware (switches, SmartNICs) and software (VNFs) in real time based on current load, accuracy requirements, and observed network conditions. The system would use continuous feedback (telemetry, queue state) to migrate or recompose function components, ensuring the right tradeoff between line-rate performance and analytic depth. This dynamic, resource-aware orchestration could unlock new performance and scalability for ML-driven network operations, and aligns with the decomposition and orchestration frameworks discussed by Moro et al. but adds a real-time, ML-centric perspective.

References:

1. P4toNFV: Offloading from P4 switches to NFV in programmable data planes. Adrián Pekár, Laszlo A. Makara, Y. Lai, Ying-dar Lin, W. Seah (2023). International Journal of Communication Systems.
2. Network Function Decomposition and Offloading on Heterogeneous Networks With Programmable Data Planes. Daniele Moro, G. Verticale, A. Capone (2021). IEEE Open Journal of the Communications Society.
3. Offloading Machine Learning to Programmable Data Planes: A Systematic Survey. R. Parizotto, B. L. Coelho, Diego Cardoso Nunes, I. Haque, Alberto E. Schaeffer-Filho (2023). ACM Computing Surveys.
4. P4toNFV: Offloading from P4 switches to NFV in programmable data planes. Adrián Pekár, Laszlo A. Makara, Y. Lai, Ying-dar Lin, W. Seah (2023). International Journal of Communication Systems.