Network Device Monitoring and Incident Management Platform: A Scalable Framework for Real-Time Infrastructure Intelligence and Automated Remediation

Abstract

In an era of increasingly complex and distributed IT infrastructures, organizations face growing challenges in maintaining continuous network availability, performance, and security. Legacy monitoring tools often fall short in providing real-time visibility, intelligent alerting, and timely incident response at scale. This paper presents a scalable framework for a Network Device Monitoring and Incident Management Platform that delivers real-time infrastructure intelligence and automated remediation. The proposed solution integrates telemetry ingestion, log analysis, and event correlation across heterogeneous network environments using cloud-native microservices. Artificial intelligence and machine learning (AI/ML) models are applied to detect anomalies, predict failures, and trigger automated remediation actions based on predefined or learned policies. The platform is designed to be extensible, fault-tolerant, and capable of integration with existing ITSM systems, enabling faster mean time to detect (MTTD) and mean time to resolve (MTTR). Performance evaluation and case studies validate the platform’s effectiveness in reducing operational overhead, enhancing system reliability, and enabling proactive network operations. This framework provides a foundational step toward intelligent, autonomous infrastructure management.