(untitled) (Journal of Computational Intelligence and Robotics, 2023)
This work aims to provide a thorough study of dynamic scaling mechanism for machine learning (ML) workloads, thereby stressing the operational trade-offs between on-site and cloud-based containerized systems. Under different workloads, this study primarily addresses performance elasticity, resource consumption efficiency, orchestration delay, and cost-effectiveness.