Automation and Intelligence in IT Operation Management - Machine Learning for Capacity Planning and Load Testing Optimization

Aug 5, 2026

The increasing complexity and scale of modern IT infrastructures necessitate strategies that preserve efficiency, reliability, and cost effectiveness. Large industrial systems require precise capacity planning to handle fluctuating demand, avoid downtime, and remain within optimal cost parameters. This dissertation proposes an agentic AIOps approach that improves maintenance and operational stability using load testing data and advanced machine learning models to enhance predictive scaling and resilience. It also addresses load-testing inefficiency by automating experiments and using early-stopping rules driven by KPI spike detection, enabling proactive, LLM-agent–supported operations across the SDLC.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages149

Reading Time6 h 15 min

AccessFree to download

Abstract

Capacity planning and IT operations stability

ML-driven predictive resource scaling

Automated load testing and early stopping

Integration with LLM agents for proactive maintenance

AUTOMATION AND INTELLIGENCE IN IT OPERATION MANAGEMENT: MACHINE LEARNING FOR CAPACITY PLANNING AND LOAD TESTING OPTIMIZATION Arthur Vitui Under the supervision of Dr. Tse-Hsun (Peter) Chen A Thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy Concordia University Montr´eal, Qu´ebec, Canada April 2025 © Arthur Vitui, 2025 CONCORDIA UNIVERSITY SCHOOL OF GRADUATESTUDIES This is to certify that the thesis prepared By: Arthur Vitui Entitled: Automation and Intelligence in ITOM: Machine Learning for Capacity Planning and Load Testing Optimization and submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science) complies with the regulations of the University and meets the accepted standards with respect to originality and quality. Signed by the final examining committee: Chair Dr. Manar Amayri External Examiner Dr. Marin Litoiu Arms-Length Examiner Dr. Ching Yee Suen Examiner Dr. Weiyi Ian Shang Examiner Dr. Diego Costa Thesis Supervisor Dr. Tse-Hsun Peter Chen Approved by Dr. Sabine Bergler, Graduate Program Director 4/24/2025 Dr. Mourad Debbabi, Dean, Gina Cody School of Engineering and Computer Science Abstract Automation and Intelligence in IT Operation Management: Machine Learning for Capacity Planning and Load Testing Optimization Arthur Vitui, Ph.D. Concordia University, 2025 The increasing complexity and scale of modern IT infrastructures necessitate innovative strategies to maintain efficiency, reliability, and cost effectiveness. Large scale industrial systems require precise capacity planning to manage fluctuating demands, prevent downtime, and operate within optimal cost parameters. However, traditional capacity planning methods often fall short in today’s dynamic environments. This dissertation introduces an agentic approach to AIOps (Artificial Intelligence for IT Operations) aimed at enhancing the maintenance and operational stability of large scale systems. Effective capacity planning is essential for stable system operations. Over provisioning leads to resource waste, while under provisioning can cause failuresand diminished performance. By utilizing load testing data and advanced machine learning (ML) models, we propose a blueprint process that optimizes system capacity planning. Integrating ML into this process enhances predictive capabilities, enabling proactive resource scaling, reducing costs, and increasing system resilience. A significant challenge in optimizing this process is the inefficiency and time consuming nature of traditional load testing. Existing methodologies often require substantial manual effort and considerable time to simulate large scale workloads. To address this, we propose a framework that streamlines load testing through automation and early stopping rules based on spike detection techniques for system Key Performance Indicators (KPIs) . By leveraging a system’s ability to predict KPI spikes, we can dynamically adjust capacity as needed. We aim to integrate these processes into tools utilized by LLM (Large Language Model) agents within an AIOps system. These tools will act as intermediaries for monitoring and maintaining large scale systems. This integration will establish a fully managed architecture, where AIOps agents enhance the IT operations team’s ability to perform proactive maintenance, respond to new incidents, autonomously monitor system health, predict potential issues, and implement proactive measures to maintain optimal performance. This dissertation presents a novel approach to enhancing efficiency in large scale systems by combining automation and load testing improvements with machine learning and LLM agents. By developing a comprehensive, scalable framework, this research seeks to reduce operational overhead and estab

FAQs

Why are traditional capacity planning methods insufficient for modern IT environments?

Modern systems face dynamic workload changes at scale, and conventional approaches often cannot adapt fast enough to prevent failures or performance degradation. This reduces both reliability and cost effectiveness.

How does the dissertation use machine learning in capacity planning?

It integrates load testing data with advanced ML models to improve predictive capabilities. The result is proactive resource scaling that reduces costs and increases system resilience.

What problem does the work address in load testing, and what is the proposed solution?

Traditional load testing can be time-consuming and inefficient, often requiring significant manual effort. The dissertation proposes automation plus early stopping rules based on spike detection for system KPIs to streamline testing while still supporting capacity decisions.

Aug 5, 2026

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