Enhancing Network Resilience Through Automated Rule-Based Monitoring, Alerting, and Proactive Mitigation

The increasing complexity of IT infrastructures, driven by the rise of IoT devices and microservices architectures, has highlighted the need for robust and scalable solutions to ensure network resilience. This study aims to guarantee system continuity in network failures by proposing a self-healing architecture that integrates monitoring, visualisation and orchestration tools for Kubernetes management. An architecture is proposed that integrates different microservices that collect real-time metrics, visualise data, generate alerts and apply corrective actions. Unlike traditional approaches limited to passive monitoring, an automated, rule-based mitigation flow is introduced, enabling detection and visualisation of failures and the implementation of corrective actions in real-time. A comparative analysis is also included with traditional approaches, highlighting key advantages such as reduced response times, elimination of human errors, better resource management and scalability in dynamic environments. Finally, limitations related to hardware capacity and the management of simultaneous alerts in multi-cluster infrastructures are identified.