AI-Augmented Data Replication Strategies for Fault-Tolerant Distributed Cloud Systems
Keywords:
AI-augmented data replication, fault-tolerant distributed systems, cloud computing, machine learning, adaptive replication, failure mitigation, predictive analytics, consistency trade-offs, resource efficiency, multi-cloud environments, scalability, real-time monitoring, intelligent frameworks, cloud resilience, high availability.Abstract
They are, inter alia, exposed distributed cloud environments, where such problem of attaining the highest fault-tolerance levels with minimum compromises on high availability and scalability is arguably among the most acute. Data replication is at the core of realizing these objectives as explained below but using standard forms of replication introduces the tradeoffs of consistency/coherency, update latencies, and resource utilization. To ensure high availability in distributed cloud system this paper presents the AI solutions for data redundancy.
References
Ahmed, S., & Hassan, M. (2021). Deep learning approaches for data replication in cloud systems. Journal of Cloud Computing, 9(4), 123–140.
Ali, R., et al. (2021). Energy-efficient cloud storage solutions: An AI-driven approach. Sustainable Computing: Informatics and Systems, 30, 100523.
Banerjee, A., et al. (2022). Explainable AI in distributed systems: A review. IEEE Transactions on Cloud Computing, 10(3), 450–465.
