Proactive Network Fault Management for Reliable Subscribed Network Slicing in Software-Defined Mobile Data IoT Services

Abstract

Proactive network solutions (PNS) become the precise management and orchestration (MANO) in the applied artificial intelligence (AI) era. The PNS proposed to invent future mobile edge communications by predicting the fault networks for reliable slicing configurations. Furthermore, federated learning (FL) systems have been appealed to apply for critical mobile data privacy of the Internet of Things (IoT) services. Therefore, FL-based IoT communications need a precise PNS to pretend the network failures to maximize the model inference and improve end-to-end (E2E) quality of services (QoS). This paper proposed an adopted software-defined network slicing (NS) for IoT communications based on network failure prediction and resource allocations by utilizing a deep-Q-network approach (DQN). The proposed proactive reliable subscribed network slicing was based on software-defined DQN-based proactive dynamic resource allocations (SDQN-PDRA) for adaptive communication configurations. The experiment showed that the proposed approach enhanced the significant outcomes of stability, reliability, convergence time, and other communication QoS.