Unlocking Reconfigurability for Deep Reinforcement Learning in SFC Provisioning

Network function virtualization (NFV) is a key foundational technology for 5G and beyond networks, wherein to offer network services, execution of Virtual Network Functions (VNFs) in a defined sequence is crucial for high-quality Service Function Chaining (SFC) provisioning. To provide fast, reliable, and automatic VNFs placement, Machine Learning (ML) algorithms such as Deep Reinforcement Learning (DRL) are widely being investigated. However, due to the requirement of fixed-size inputs in DRL models, these algorithms are highly dependent on network configuration such as the number of data centers (DCs) where VNFs can be placed and the logical connections among DCs. In this study, a novel approach using the DRL technique is proposed for SFC provisioning which unlocks the reconfigurability of the networks i.e. the same proposed model can be applied in different network configurations without additional training. Moreover, an advanced Deep Neural Network (DNN) architecture is constructed for DRL with an attention layer that improves the performance of SFC provisioning while considering the efficient resource utilization and the End-to-End (E2E) delay of SFC requests by looking up their priority points. Numerical results demonstrate that the proposed model surpasses the baseline heuristic method with an increase in the overall SFC acceptance ratio by 20.3% and a reduction in resource consumption and E2E delay by 50% and 42.65%, respectively.