Performance Bottleneck Detection and Root Cause Analysis Using Explainable AI

• Author(s): Shanmugasundaram Sivakumar
• Paper ID: 1704282
• Page: 1005-1011
• Published Date: 30-04-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 10 April-2023

Abstract

Performance bottlenecks in complex systems can significantly hinder operational efficiency and user satisfaction. These bottlenecks often result from various factors, including resource limitations, inefficient algorithms, or suboptimal system configurations. Detecting and analyzing these issues promptly is crucial for maintaining system integrity and performance (Adadi & Berrada, 2018). This research explores the integration of Explainable Artificial Intelligence (XAI) in identifying and addressing the root causes of performance bottlenecks, providing a framework that enhances interpretability and trust in AI-driven solutions. Our study leverages advanced machine learning algorithms coupled with interpretability techniques to provide actionable insights into performance issues. Through empirical testing and analysis, we demonstrate that XAI significantly improves the detection accuracy of performance bottlenecks compared to traditional methods, enabling organizations to implement timely interventions (Lipton, 2016). Furthermore, XAI fosters a deeper understanding of the decision-making processes behind automated systems, empowering stakeholders to make informed choices regarding system optimization. We present a comprehensive methodology for applying XAI to performance data, detailing the steps involved in detecting bottlenecks and analyzing their root causes. Our findings indicate that organizations employing XAI in their performance management strategies not only benefit from enhanced detection capabilities but also from improved user trust and satisfaction. This paper contributes to the growing body of knowledge on XAI by providing empirical evidence of its effectiveness in performance optimization, paving the way for future research in this critical area.

Keywords

Performance Bottlenecks, Root Cause Analysis, Explainable AI, Performance Optimization, Machine Learning

Citations

IRE Journals:
Shanmugasundaram Sivakumar "Performance Bottleneck Detection and Root Cause Analysis Using Explainable AI" Iconic Research And Engineering Journals Volume 6 Issue 10 2023 Page 1005-1011

IEEE:
Shanmugasundaram Sivakumar "Performance Bottleneck Detection and Root Cause Analysis Using Explainable AI" Iconic Research And Engineering Journals, 6(10)