• Use Case: Optimize the cost of Kubernetes workloads by automating the scaling of pods and nodes based on demand.

• The platform can ensure that resources are provisioned efficiently to meet demand without overprovisioning, thereby optimizing costs.

• Integrating with Kubernetes HPA for automatic pod scaling based on real-time metrics.

• Managing and automating the deployment of Cluster Autoscaler to scale the underlying EC2 nodes.

• Monitoring and optimizing resource allocation using VPA (Vertical Pod Autoscaler).

• Implementing scaling policies and automation workflows to ensure cost-efficient and responsive scaling operations.

• Enabling auto-scaling for serverless workloads with Fargate profiles, reducing the need for manual scaling interventions.

Tasks that reuse 'DagKnows proxy setup instructions for docker'