Investigating the Most Effective AI/ML-Based Strategies for Predictive Network Maintenance to Minimize Downtime and Enhance Service Reliability

Abstract

The increasing complexity of modern network infrastructures presents significant challenges in maintaining performance and service reliability. Traditional reactive maintenance approaches, which rely on manual troubleshooting and scheduled checks, are often insufficient to prevent unplanned downtime, resulting in financial losses and decreased customer satisfaction. This research proposal explores the most effective AI/ML-based strategies for predictive network maintenance to minimize downtime and enhance service reliability. By leveraging advanced data-driven models, AI and ML technologies can forecast network failures, detect anomalies, and optimize resource allocation, enabling proactive management of network operations. The primary objectives of this study are to identify the most effective AI/ML algorithms, develop predictive models capable of real-time failure forecasting, and assess the impact of these strategies on network performance. The research will evaluate various algorithms, including time-series forecasting (LSTM, ARIMA), supervised learning (Random Forest, SVM), and unsupervised learning models for anomaly detection. By combining historical network data analysis with simulations, this study aims to build a scalable framework for predictive network maintenance. The findings are expected to provide actionable insights, guiding organizations in adopting AI-driven network automation solutions to enhance operational efficiency, reduce costs, and improve network resilience, ultimately supporting the growing demand for reliable digital connectivity across multiple sectors.

Keywords: Predictive Network Maintenance, Artificial Intelligence (AI), Machine Learning (ML), Network Downtime, Service Reliability, Network Automation, Anomaly Detection, Time-Series Forecasting, AI/ML Algorithms, Proactive Network Management, Network Resilience, Data-Driven Insights, Network Optimization, Operational Efficiency