CPU Student Publishes Breakthrough Research on AI-Optimized Cloud Infrastructure

Showcasing the growing spirit of research and innovation at Career Point University, Keshav Sharma, a student of the Integrated B.Tech–M.Tech Computer Science and Engineering program has successfully published a research paper in the prestigious International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET). His work, titled “Integrating Machine Learning for Dynamic Resource Allocation and Failure Prediction in Containerized Cloud CI/CD Pipelines,” presents a novel approach to enhancing the efficiency of cloud-based software development systems through machine learning.

The research, completed under the supervision of Mr. Deepak Mahawar, explores the application of machine learning models to improve efficiency and reliability in cloud-based CI/CD environments. Using techniques such as Random Forest and SVM, the study introduces a hybrid framework that integrates predictive analytics with Kubernetes for real-time failure detection and intelligent resource management.

Performance testing revealed impressive results, including a 60% reduction in unplanned pipeline interruptions and a 40% improvement in resource utilization, making the research highly relevant to modern DevOps and cloud infrastructure practices.

This accomplishment underscores the university’s commitment to nurturing research talent and supporting innovations that address real-world challenges. It also highlights the School of Computer Science & Engineering’s ongoing efforts to bridge academic learning with evolving industry needs.

Career Point University congratulates Keshav Sharma and his mentor for this impactful contribution and reaffirms its focus on fostering a strong research culture across all academic disciplines.