N.B. EVENT POSTPONED Enhancing DevOps with Autonomous Monitors: A Proactive Approach to Failure Detection

Adha Hrusto will have her PhD Pre-seminar on 5 April 2024 in E:2116.

Abstract: Continuous practices, including continuous integration, continuous testing, and continuous deployment, aim to streamline and automate the software development process. A cultural and professional movement that builds upon continuous practices, DevOps, seeks to bridge the gap between development and operations. By fostering a collaborative environment, DevOps supports faster, more frequent, and reliable software releases, inherently promoting agile methodologies throughout the software deployment lifecycle. By introducing agility, there is a higher risk of operational failures. Recognizing this challenge, the objective of this thesis is to understand and present approaches for mitigating the cascading effects of operational failures across interconnected software components.

Through collaboration with two Swedish companies, it was investigated how proactive monitoring strategies inspired by state-of-the-art machine learning (ML) solutions can prevent failure propagation and ensure seamless software operations.
The main contributions of this thesis are threefold. First, the two case studies resulted in a better understanding of the challenges in operations, which enabled defining problem constructs related to monitoring data overflow and alert management.
Second, based on extensive observations of the complex cloud-based software systems and recent contributions of academic research, a new proactive alert strategy was proposed and implemented to identify the early stages of failure propagation. This technical solution, referred to as autonomous monitors, can ensure continuous oversight of multidimensional software systems while predicting and mitigating anomalies before they affect operations.  Finally, the integration of autonomous monitors was empirically demonstrated to be beneficial through evaluations conducted in two case companies and a survey study assessing their applicability in diverse industrial settings.

The thesis contributes to understanding operational challenges, developing innovative monitoring solutions, and empirically demonstrating the benefits of integrating these solutions in real-world settings. It emphasizes the potential of machine learning in enhancing DevOps practices, aiming for broader adoption in improving software development and operational sustainability.

Very welcome!