Optimizing Fault-Tolerance in Distributed Systems with AI-Augmented Replica Management - Kalesha Khan Pattan (International Journal of Intelligent Systems and Applications in Engineering, 2021)
Fault tolerance is a fundamental requirement in distributed systems to ensure reliability, consistency, and continuous service availability despite hardware or software failures. Traditional replica management techniques, such as static replication and consensus-based recovery, provide basic fault resilience but are limited by fixed thresholds, redundant overhead, and slow adaptation to dynamic workloads or network variations. This research proposes an AI-augmented replica management framework that integrates machine learning and predictive analytics to enhance fault tolerance adaptively. The proposed approach continuously monitors system metrics—such as node health, latency, throughput, and communication reliability—and uses learning models to predict potential node failures or performance degradation before they occur. Based on these predictions, the system dynamically adjusts the replication factor, placement, and synchronization frequency of replicas to maintain service continuity while minimizing resource overhead. Reinforcement learning algorithms guide replica redistribution decisions by balancing fault coverage and cost efficiency in real time. In addition, the framework leverages anomaly detection models to identify early warning signs of hardware instability, resource contention, and network congestion. By employing deep learning techniques, the system learns long-term behavioral patterns of nodes, enabling proactive fault prevention rather than reactive recovery. The integration of AI ensures that replica placement and recovery strategies evolve continuously as the system environment changes. A multi-objective optimization function is used to strike a balance between reliability, latency, and energy efficiency. Simulation results on large-scale distributed clusters demonstrate that the AI-based model significantly improves recovery time, prediction accuracy, and system throughput when compared to conventional replica management methods. This research contributes an intelligent, self-healing replication framework that enhances resilience, scalability, and autonomy in distributed architectures, enabling proactive fault management and optimal resource utilization across modern cloud, edge, and IoT ecosystems.