Installing Edera on Google Compute Engine (GCE) – Edera
Installing Edera on Google Compute Engine (GCE)
Edera deploys as a container runtime which GKE managed-nodes do not support. Edera is fully compatible on GCP nodes and can integrate with any Kubernetes control-plane. See below for more details installing Edera with Kubernetes on GCP.
GCE recommendations
- Use any Linux distribution except Container-Optimized OS (COS)
- Works on all GCE instances
Installing Edera on Google Compute Engine follows the same process as any Linux installation.
👉 Start with: Run the Edera installer guide.
Kubernetes integration for GCE
After installing Edera, you’ll need to configure Kubernetes to use Edera’s container runtime interface (CRI) at /var/lib/edera/protect/cri.socket.
New Kubernetes cluster with kubeadm
Initialize your cluster with Edera’s CRI socket:
kubeadm init --cri-socket=unix:///var/lib/edera/protect/cri.socket
For worker nodes:
kubeadm join [control-plane-ip:port] --token [token] --discovery-token-ca-cert-hash [hash] --cri-socket=unix:///var/lib/edera/protect/cri.socket
Existing kubelet configuration
Configure kubelet to use Edera’s CRI socket by adding:
--container-runtime-endpoint=unix:///var/lib/edera/protect/cri.socket
Option 1: Service file
Edit kubelet service:
sudo systemctl edit kubelet
Add:
[Service]
Environment="KUBELET_EXTRA_ARGS=--container-runtime-endpoint=unix:///var/lib/edera/protect/cri.socket"
Option 2: Configuration file
Edit /var/lib/kubelet/config.yaml:
containerRuntimeEndpoint: unix:///var/lib/edera/protect/cri.socket
Important: CLI options take precedence over configuration files. If your kubelet is already configured with --container-runtime-endpoint via command line arguments, remove that option or the configuration file setting will be ignored.
Restart kubelet:
sudo systemctl daemon-reload
sudo systemctl restart kubelet
Important notes
Unsupported environments
Edera does not support k3d, kind, or minikube because they don’t allow CRI socket configuration.
What’s next
Edera is now running on your GCE instance with hypervisor-level container isolation.
Next Steps
- Set up monitoring to track your workloads
- Kernel customization for advanced kernel configuration
- Explore GPU support for AI/ML workloads on GCP
Additional Resources
- GCP deployment options - More Google Cloud guides
- Technical deep-dive - How Edera works under the hood
Need Help?
- Email support@edera.dev - We’re here to help
- Google Cloud documentation for GCE-specific issues