AI-Driven Fiber Channel Congestion Identification and Mitigation: Enhancing SAN Network Reliability via Intelligent Automation
DOI:
https://doi.org/10.63075/tvamw103Keywords:
Artificial Intelligence, Fibre Channel Congestion, Buffer Credit Recovery, Autonomous Network Remediation, Predictive SAN Management, Intelligent Automation, Machine Learning in SANs, Neural Network Optimization, Dynamic Path Allocation, Proactive Congestion Resolution,Abstract
Fibre Channel Storage Area Networks (SANs) have consistently faced congestion challenges that hinder performance and disrupt vital enterprise operations. This study examines the transformative impact of artificial intelligence in reshaping congestion detection and mitigation within FC environments. Utilizing advanced machine learning techniques and neural network architectures, AI-based frameworks can autonomously correlate diverse network anomalies, precisely identify the underlying causes of credit stalls, and execute automated remediation without human involvement. The adoption of AI-powered analytics fosters a paradigm shift from reactive fault resolution to proactive network governance, effectively eliminating recurring congestion through intelligent buffer credit allocation and adaptive path optimization. Enterprises deploying these solutions achieve markedly higher network resilience, improved application responsiveness, and lower operational complexity, solidifying AI as a cornerstone of modern SAN infrastructure management.
