Leveraging Machine Learning for Database Pool Health: Intelligent Monitoring and Remediation Strategies for Enterprise Applications

EasyChair Preprint 15026

Abstract

In the realm of enterprise applications, maintaining optimal database pool health is crucial for ensuring system performance and reliability. This article explores advanced methodologies for leveraging machine learning (ML) to enhance database pool monitoring and remediation strategies. We delve into various ML techniques that can predict potential issues, optimize resource allocation, and automate responses to anomalies in real-time. By integrating intelligent algorithms with traditional monitoring tools, organizations can achieve proactive management of their database pools. The paper presents a comprehensive framework that includes data collection, feature engineering, model training, and deployment strategies. Case studies illustrate the effectiveness of these ML-driven approaches in reducing downtime, improving system responsiveness, and minimizing operational costs. Through a detailed analysis of implementation practices and outcomes, this article provides valuable insights for enterprises aiming to harness the power of machine learning to fortify their database pool health and enhance overall application performance.

Keyphrases: Application, Health, Performance, database, learning, machine, pool, power

@booklet{EasyChair:15026,
  author    = {Anthony Collins},
  title     = {Leveraging Machine Learning for Database Pool Health: Intelligent Monitoring and Remediation Strategies for Enterprise Applications},
  howpublished = {EasyChair Preprint 15026},
  year      = {EasyChair, 2024}}