Developing an Intelligent Framework for Improving the Reliability and Resilience of Enterprise WAN Networks Using Artificial Intelligence and Predictive Analytics

Enterprise Wide Area Networks (WANs) have become critical digital infrastructures for organizations whose business processes increasingly depend on cloud applications, distributed information systems, remote branches, digital services, real-time collaboration, and geographically dispersed users. As the number of connected sites, applications, network devices, service dependencies, and traffic patterns increases, conventional monitoring and reactive fault-management approaches become less capable of maintaining reliable and resilient network operations. This article develops an intelligent conceptual framework for improving the reliability and resilience of enterprise WAN networks through the integration of Artificial Intelligence (AI), Machine Learning (ML), predictive analytics, Software-Defined Networking (SDN), Software-Defined Wide Area Networking (SD-WAN), network telemetry, anomaly detection, automated diagnosis, and closed-loop remediation. The study adopts an analytical literature-review approach and synthesizes established research in network resilience, machine learning for networking, network traffic prediction, AIOps, SDN, SD-WAN, network digital twins, reinforcement learning, and explainable AI. The proposed framework consists of six interconnected layers: multi-source telemetry and data acquisition, data engineering and feature construction, predictive intelligence, anomaly and failure-risk detection, decision and optimization, and adaptive recovery and continuous learning. The framework emphasizes proactive rather than purely reactive network management by identifying emerging degradation patterns before they become service-affecting failures. The article also proposes an evaluation model based on reliability, resilience, service continuity, prediction accuracy, detection quality, recovery time, false-alarm rate, and operational efficiency. Finally, the study identifies major challenges associated with data quality, concept drift, model explainability, cybersecurity, automation risk, legacy infrastructure, interoperability, and organizational readiness, and presents future research directions for autonomous and resilient enterprise WAN management.

کلیدواژه ها:

Enterprise WAN ، Artificial Intelligence ، Predictive Analytics ، Network Resilience