Using Reinforcement Learning to Optimize Data Replication Policies in Distributed Database Architectures

Abstract

In the era of big data, distributed database systems have become integral for managing vast volumes of information across diverse environments. Efficient data replication strategies are critical to ensuring high availability, fault tolerance, and improved performance. This paper explores the application of Reinforcement Learning (RL) techniques to optimize data replication policies in distributed database architectures. By formulating the replication problem as a Markov Decision Process (MDP), we propose a novel RL-based approach that dynamically adjusts replication strategies based on workload patterns and system states. Experimental results demonstrate significant improvements in performance metrics, including response time and resource utilization, compared to traditional static replication strategies.