Network function parallelism configuration with segment routing over IPv6 based on deep reinforcement learning

Abstract

Network function parallelism (NFP) has gained attention for processing packets in parallel through service functions arranged in the required service function chain. While parallel processing efficiently reduces the service function chaining (SFC) completion time, it incurs a higher network overhead (e.g., network congestion) to replicate various packets for processing. To reduce the SFC completion time while maintaining a low network overhead, we propose a deep-reinforcement-learning-based NFP algorithm (DeepNFP) that provides an SFC processing policy to determine the sequential or parallel processing of every service function. In DeepNFP, deep reinforcement learning captures the network dynamics and service function conditions and iteratively finds the SFC processing policy in the network environment. Furthermore, an SFC data plane protocol based on segment routing over IPv6 configures and operates the policy in the SFC data network. Evaluation results show that DeepNFP can achieve 46% of the SFC completion time and 66% of the network overhead compared with conventional SFC and NFP, respectively.