Implementing Intelligent AI/ML Systems for Efficient Database Pool Connection Monitoring and Anomaly Resolution

EasyChair Preprint 15020

Abstract

In the rapidly evolving landscape of database management, maintaining optimal performance and reliability is increasingly challenging due to the growing complexity of database systems and the sheer volume of data transactions. This paper explores the implementation of advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the monitoring and anomaly resolution processes for database pool connections. We present a comprehensive framework that integrates AI/ML algorithms to dynamically analyze connection patterns, detect anomalies, and automate responses to potential issues. Our approach leverages supervised learning models to identify patterns indicative of performance degradation or failures and unsupervised learning techniques for anomaly detection in real-time. We also discuss the deployment of reinforcement learning strategies to optimize the resolution of identified anomalies, thus minimizing downtime and improving overall system efficiency. Through experimental validation and performance evaluation, we demonstrate significant improvements in monitoring accuracy, anomaly detection speed, and system resilience. This paper provides valuable insights into how intelligent AI/ML systems can revolutionize database pool management by preemptively addressing issues before they impact system performance.

Keyphrases: AI, Performance, database, management, pool, system

@booklet{EasyChair:15020,
  author    = {Wayzman Kolawole},
  title     = {Implementing Intelligent AI/ML Systems for Efficient Database Pool Connection Monitoring and Anomaly Resolution},
  howpublished = {EasyChair Preprint 15020},
  year      = {EasyChair, 2024}}