Dynamic Scaling of Machine Learning Workloads: A Comparative Study of On-Prem and Cloud-Based Containers
Keywords:
dynamic scaling, machine learning workloads, containers, Kubernetes, resource orchestrationAbstract
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.
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