P4toNFV: Offloading from P4 switches to NFV in programmable data planes

P4 combines the benefits of hardware-based networking with the adaptability of software-based network operations. However, when faced with intricate network functions, P4 switches reveal constraints in memory and processing primitives. To address these, we advocate offloading traffic demanding intricate processing from the programmable data plane to network function virtualization (NFV). By leveraging this approach, P4 switches handle the core data plane, ensuring maximum performance, whereas virtualized network functions (VNF) cater to the intricate processing. Central to our research is the optimization of this offloading process, specifically considering delay constraints. We developed an analytical model that examines a P4 switch overseen by an SDN controller, integrating an offloading capability to NFV. The principal objective was to determine an offloading rate that minimizes packet processing delay. To this end, we employed a Bounded method, an advancement from Brent's method, to determine this optimal rate. The findings indicate that offloading approximately 66% of packets to the VNF achieves the lowest total delay, registering at 0.1505 mu$$ \upmu $$s. This strategy of optimal offloading can notably reduce the system's average delay as the demand for network functions increases. The optimization technique we adopted exhibited rapid convergence in our experiments, reflecting the method's efficacy. Furthermore, a rigorous parametric sensitivity analysis spanning no offloading, full offloading, and optimal offloading strategies underscores that optimal offloading to NFV consistently augments system performance, particularly in terms of delay reduction. Conclusively, our study furnishes valuable insights into offloading strategies, augmenting the narrative on resource allocation in both PNFs and VNFs. P4 switches, which harness both the robustness of hardware networking and the flexibility of software operations, grapple with intricate processing tasks. To tackle this, our research introduces an analytical model for efficient traffic offloading to NFV, utilizing a Bounded adaptation of Brent's optimization technique. Employing parametric sensitivity analysis, our investigations underscore the strategy's resilience and efficacy, highlighting a 66% offloading rate to VNF as optimal, markedly minimizing delay for the evaluated parameter set. image