Implementing Self-Healing Mechanisms in Adaptive Systems to Address Network Failures

Abstract

Network failures in adaptive systems can lead to service disruptions and reduced performance, necessitating mechanisms that ensure system resilience. This paper explores the integration of self-healing mechanisms to autonomously detect, diagnose, and recover from network failures, minimizing downtime and human intervention. The proposed architecture utilizes real-time monitoring, machine learning-based anomaly detection, and dynamic reconfiguration to address failures as they occur. Case studies in cloud computing and IoT networks demonstrate significant improvements in system stability and reduced recovery times compared to traditional fault-tolerance methods. Despite its advantages, self-healing systems face challenges related to scalability, security, and adaptability to emerging technologies. This paper outlines the implementation of self-healing mechanisms, evaluates their performance, and discusses future research directions aimed at enhancing system resilience in increasingly complex network environments.