A Machine learning-based Framework to Support Monoliths to Microservices Migration - From Identiﬁcation to Packaging and Deployment

Aug 5, 2026

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The monolith-to-microservices migration is driven by the need for scalable, maintainable and agile software systems. Despite the benefits of microservices such as independent deployment, failure isolation, and improved scalability, migrating an existing monolith remains complex, resource-intensive, and error-prone across multiple interdependent phases: pre-migration, service identification, service packaging, deployment, and monitoring. Existing approaches rely heavily on expert-guided manual techniques, which are costly and slow.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages225

Reading Time9 h 27 min

AccessFree to download

Résumé

Problématique et phases de migration

Limites des approches existantes

Apprentissage automatique pour automatiser la migration

A Machine learning-based Framework to Support Monoliths to Microservices Migration: From Identiﬁcation to Packaging and Deployment by Imen TRABELSI THESIS PRESENTED TO ÉCOLE DE TECHNOLOGIE SUPÉRIEURE IN PARTIAL FULFILLMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Ph.D. MONTREAL, 18 AUGUST 2025 ÉCOLE DE TECHNOLOGIE SUPÉRIEURE UNIVERSITÉ DU QUÉBEC Imen TRABELSI, 2025 This Creative Commons license allows readers to download this work and share it with others as long as the author is credited. The content of this work cannot be modiﬁed in any way or used commercially. BOARD OF EXAMINERS THIS THESIS HAS BEEN EVALUATED BY THE FOLLOWING BOARD OF EXAMINERS Mrs. Naouel Moha, Thesis supervisor Department of Software and IT Engineering, École de technologie supérieure ÉTS Mr. Yann-Gaël Guéhéneuc, Thesis Co-Supervisor Departement of Computer Science and Software Engineering, Concordia university Mr. Lokman Sboui, Chair, Board of Examiners Department of System Engineering, École de technologie supérieure ÉTS Mrs. Ghizlane El Boussaidi, Member of the Jury Department of Software and IT Engineering, École de technologie supérieure ÉTS Mr. Julien Gascon-Samson, Member of the Jury Department of Software and IT Engineering, École de technologie supérieure ÉTS Mr. Mohamed Aymen Saied, External Independent Examiner Department of Computer Science and Software Engineering, Laval university THIS THESIS WAS PRESENTED AND DEFENDED IN THE PRESENCE OF A BOARD OF EXAMINERS AND THE PUBLIC ON 18 AUGUST 2025 AT ÉCOLE DE TECHNOLOGIE SUPÉRIEURE IV ACKNOWLEDGEMENTS I am deeply grateful to my supervisors, Prof. Naouel Moha and Prof. Yann-Gaël Guéhéneuc, for their invaluable guidance, continuous support, and generous mentorship. Their complementary expertise, constructive feedback, and unwavering belief in my work have profoundly shaped the direction and quality of this research. I feel privileged to have worked under their supervision. I also warmly thank all my colleagues from the ptiDej team for their collaboration, helpful discussions, and the friendly and inspiring work atmosphere they created. Your support and companionship have made this journey both productive and enjoyable. I would like to sincerely thank the members of the jury for accepting to evaluate my thesis and for their insightful comments: Prof. Lokman Sboui, Prof. Mohamed Aymen Saied, Prof. Ghizlane El Boussaidi, and Prof. Julien Gascon-Samson. My sincere thanks go to the Department of Software and IT Engineering at ÉTS and the Department of Computer Science and Software Engineering at Concordia University for providing a rich and supportive research environment. To Oussama, my partner, my anchor, and my greatest source of strength, thank you. We’ve been through so much together, and yet you stood by me at every step, with patience, love, and unwavering support. In moments of doubt, you reminded me of my worth; in moments of exhaustion, you gave me courage. This journey would have been unimaginable without you by my side. To my beloved parents, Adel and Khaoula, thank you for supporting me all along my life, for always believing in me, and for sacriﬁcing so much for us. Your love, strength, and resilience have been my foundation. This achievement is as much yours as it is mine, and I am endlessly grateful for everything you have done to help me reach this point. To my dear sisters, Tesnim, Sirine, and May, I may be the oldest, but I never felt the pressure of being so, because your love, support, and presence always lifted me up. Thank you for being my VI cheerleaders, my safe space, and my constant source of joy. Your strength and kindness have inspired me more than you know, and I am so proud to walk this path with you by my side. To my grandmother, the peaceful soul I lost during this journey, your absence was deeply felt every step of the way. You will always be missed, and your memory lives on in my heart. To my

FAQs

Why is migration from monoliths to microservices considered complex?

It involves multiple interdependent phases—pre-migration, identification, packaging, deployment, and monitoring—making it resource-intensive and error-prone when transforming an existing system.

What do existing migration approaches typically rely on?

They largely depend on expert-guided manual techniques, which increases cost, prolongs timelines, and raises the risk of mistakes.

How can machine learning help in the migration process?

Recent progress enables analyzing source code, learning patterns from existing systems, and generating source code or configuration files to automate and optimize key migration phases.

Aug 5, 2026

Upload by Quinn