Workload-Based Performance Tuning in Database Management Systems through Integration of Artificial Intelligence

Abstract

Traditional methods of performance tuning in Database Management Systems (DBMS) are facing significant challenges in adapting to the dynamic nature of modern workloads. Reactive approaches and static configurations often lead to performance bottlenecks and inefficient resource utilization. In response, this paper proposes a novel approach for workload-based performance tuning through the integration of Artificial Intelligence (AI). By leveraging AI techniques such as machine learning and predictive modeling, the proposed methodology aims to automate the analysis of workload patterns, predict future trends, and dynamically adjust DBMS configurations for optimal performance. The paper discusses the key components of the proposed methodology, including workload characterization, predictive modeling, and adaptive configuration management. A hypothetical case study in an e-commerce database environment illustrates the implementation and potential performance improvements achieved through AI-powered tuning. Furthermore, the paper explores real-world applications, future research directions, challenges, and best practices for implementing workload-based tuning with AI integration. Overall, this paper presents a comprehensive framework for leveraging AI to enhance DBMS performance, scalability, and efficiency in dynamic environments.