Exploring the Impact of Data Locality in Distributed Databases: A Machine Learning-Driven Approach to Optimizing Data Placement Strategies

Abstract

Data locality significantly influences the performance of distributed databases, affecting query response times and resource utilization. This study investigates the role of data locality in enhancing the efficiency of distributed systems through a machine learning-driven approach to optimize data placement strategies. By analyzing access patterns, network latencies, and computational loads, we develop predictive models that inform dynamic data placement decisions. Utilizing reinforcement learning algorithms, the study adapts to fluctuating workloads, effectively minimizing data transfer times and maximizing throughput. Empirical results illustrate substantial improvements in query performance and resource management, highlighting the efficacy of intelligent data locality strategies. This study paves the way for future advancements in artificial intelligence-driven optimization for distributed database architectures.