Container Orchestration for AI — Kubernetes Cost Optimization

Key Points

• The research aims to identify strategies for reducing costs in Kubernetes deployments specifically for artificial intelligence applications.
• Analysis of various cost management techniques in Kubernetes.
• Evaluation of resource allocation strategies for AI workloads.
• Comparison of performance metrics before and after implementing optimization methods.
• Identified key areas where costs can be reduced in Kubernetes environments.
• Demonstrated significant cost savings through improved resource allocation for AI applications.
• Showed enhanced efficiency in managing AI workloads with optimized settings.

Abstract