Distributed Infinity (United States)

Distributed databases have been an important foundation point for scalable high-throughput applications for a while now. However, as we approach data deluge and application complexity, the traditional ways of optimization may fail to guarantee performance, scalability, and fault tolerance. This paper discusses an extensive inclusion of Artificial Intelligence (AI) approaches to distributed database systems to improve query processing and replication strategies in order to overcome major performance bottlenecks. The conventional optimization techniques to query are purely based on static cost models and fixed heuristics, which, most of the time, are unable to cope with dynamic workloads. Correspondingly, the static replication strategies are not able to effectively deal with the dynamic access patterns of contemporary applications. The reason for its study is the limited possibilities of the existing approaches and the possibilities of artificial intelligence to change the way distributed databases deal with resources and queries. We propose a new AI-enhanced architecture for distributed databases with an application of Machine Learning (ML) and Deep Learning (DL) that is able to: Forecast queries execution plans on the basis of history, Optimize real-time replication strategies, Dynamically adapt to workload changes, and Improve the fault tolerance and system robustness. Experimental assessments on a simulated high-throughput e-commerce workload show that AI-enhanced systems overperform legacy setups by up to 40% for query latency and up to 30% better replication efficiency. These enhancements are validated with benchmark standards in the industry