Self-Healing Networks: An AI Approach to Network Fault Management

Abstract

As networks get more intricate and essential to contemporary communication, it is imperative to guarantee their dependability and robustness. Self-healing networks offer a promising way to reduce network downtime and enhance service continuity since they can automatically identify, diagnose, and fix problems. With an emphasis on fault detection, isolation, and recovery, this research investigates the incorporation of Artificial Intelligence (AI) approaches into self-healing networks. We examine current methods for self-healing network systems, emphasizing important artificial intelligence techniques like machine learning, anomaly detection, and reinforcement learning, as well as how they might be used to handle network faults. To improve fault prediction and automated recovery procedures, a hybrid AI-based architecture is put forth that incorporates several AI methodologies. The effectiveness of the suggested system in cutting down on fault resolution time is shown by experimental findings. The scalability, adaptability, and real-time operation of AI models are among the difficulties in putting AI-driven self-healing systems into practice that are covered in the study. It also talks about the possibility of combining self-healing features with cutting-edge network technologies like Software-Defined Networking (SDN) and 5G. The study concludes by outlining upcoming research avenues that seek to use AI advancements to further improve network resilience.

Keywords: Self-Healing Networks, Artificial Intelligence, Network Fault Management, Machine Learning, Anomaly Detection, Reinforcement Learning, Network Resilience, Software-Defined Networking (SDN), Fault Recovery, Fault Detection, Hybrid AI Systems, Network Reliability, AI-Based Frameworks, Autonomous Network Management