• AWS EKS costs can escalate due to massive, parallel workloads in life sciences/drug development (e.g., genomic sequencing, molecular modeling).
• Default Kubernetes scheduler uses leastAllocated strategy, spreading pods across many nodes for fairness/high availability.
• leastAllocated strategy causes many partially utilized nodes, preventing autoscalers from scaling down idle nodes, increasing costs.
• mostAllocated scheduling strategy "packs" pods onto fewer nodes, maximizing utilization and enabling autoscalers like Karpenter to remove idle nodes.
• Switching to mostAllocated can reduce runtime costs significantly (e.g., ~10% in UAT, 43% in PROD environments).
• Custom scheduler deployment on AWS EKS requires creating a service account, ClusterRoleBindings, RoleBinding, a ConfigMap with the mostAllocated scoring strategy, and a deployment with a matching Kubernetes version container image.
• Resource weights can prioritize packing of expensive resources (e.g., high weight on GPUs for ML workloads).
• Testing in non-production environments is recommended before full rollout.
• Implementing mostAllocated scheduling can dramatically optimize costs by enabling cluster autoscalers to shut down unused nodes.

Karpenter

Key limitation of CAS