A three-dimensional scheme for elastic scaling of Kubernetes
Coping with high concurrency scenarios requires scaling at three levels:
- Horizontal expansion:
- Modify the Deployment's
replicasParameter (recommended initial value 3) - Configure HPA for automatic expansion and contraction:
kubectl autoscale deployment mcp-deployment --cpu-percent=70 --min=3 --max=10
- Modify the Deployment's
- Resource optimization:
- Setting resource requests/restrictions in the container specification:
resources:
requests:
cpu: "500m"
memory: "512Mi" - Balancing Node Load with K8s Topology Distribution Constraints
- Setting resource requests/restrictions in the container specification:
- Flow management:
- Configure load balancing via Ingress (Nginx Ingress recommended)
- Maintaining a session using Service's sessionAffinity
Special Note: For stateful services such as Claude, you need to work with PV/PVC to achieve persistent storage.
This answer comes from the articleMCP Containers: Hundreds of MCP Containerized Deployments Based on DockerThe
May not be reproduced without permission:AI productivity tools " How to scale MCP Container Services to support high concurrent AI requests in a Kubernetes environment?
