A Novel Framework for Low-Latency Strong Consistency in Geo-Distributed Databases
Keywords:
Geo-distributed databases, latency, strong consistency, machine learning, hybrid consensus.Abstract
This paper presents a novel framework for balancing latency and strong consistency in geo-distributed databases. The proposed framework leverages machine learning to dynamically adapt to network conditions, ensuring low-latency data synchronization while maintaining strong consistency across geographically dispersed regions. By integrating a hybrid consensus protocol, the framework optimizes data replication strategies, improving system performance in high-latency environments. Additionally, it demonstrates the effectiveness of the framework in real-world applications such as financial systems, e-commerce platforms, and IoT systems. The evaluation results show significant improvements in response time and consistency compared to existing consensus protocols like Paxos and Raft. Future work will explore optimizing latency-consistency trade-offs using advanced machine learning techniques and further integrating emerging technologies like edge computing and blockchain.
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