Intelligent Data Synchronization Strategies for Geo-Distributed Systems

  • Authors

    • Dr. Geetha Ramasamy Associate Professor, Madurai Kamaraj University, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2025PI3N7F

    Published 04-04-2025

  • Geo-Distributed Systems, Data Synchronization, Intelligent Replication, Machine Learning, Adaptive Consistency, Distributed Databases, Cloud Computing, Predictive Analytics

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    G. Ramasamy, “Intelligent Data Synchronization Strategies for Geo-Distributed Systems”, IJADSMC, vol. 8, no. 1, pp. 01–15, Apr. 2025, doi: 10.67228/30713498/IJADSMC-2025PI3N7F.

  • Abstract

    Geo-distributed systems are essential for modern cloud computing, enabling applications to run across multiple geographically distributed data centers while ensuring scalability, fault tolerance, and low latency. However, maintaining data consistency and synchronization across distributed nodes remains challenging due to network delays, bandwidth limitations, node failures, and dynamic workloads. This study proposes an intelligent data synchronization framework that integrates machine learning-based workload prediction, adaptive replication management, conflict-aware synchronization, and latency-aware consistency optimization. The framework continuously monitors system metrics such as network latency, bandwidth usage, replication delay, transaction rates, and node availability to make dynamic synchronization decisions. By forecasting future data access patterns and adapting synchronization strategies accordingly, the proposed model reduces replication traffic, synchronization conflicts, and resource consumption while maintaining consistency. Experimental results demonstrate significant improvements over traditional methods, achieving approximately 94% prediction accuracy, 35% lower synchronization overhead, 28% higher throughput, and 31% lower latency. The framework provides a scalable and efficient solution for cloud computing, edge computing, IoT, and large-scale enterprise systems. Future work includes integrating Federated Learning, autonomous synchronization orchestration, and blockchain-based consistency management for decentralized environments.

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