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Version: 1.5

Workload deployment

The following instructions will guide you through the process of making an existing Kubernetes deployment confidential and deploying it together with Contrast.

A running CoCo-enabled cluster is required for these steps, see the setup guide on how to set up a cluster on AKS.

Deploy the Contrast runtime​

Contrast depends on a custom Kubernetes RuntimeClass (contrast-cc), which needs to be installed in the cluster prior to the Coordinator or any confidential workloads. This consists of a RuntimeClass resource and a DaemonSet that performs installation on worker nodes. This step is only required once for each version of the runtime. It can be shared between Contrast deployments.

kubectl apply -f https://github.com/edgelesssys/contrast/releases/download/v1.5.1/runtime-aks-clh-snp.yml

Deploy the Contrast Coordinator​

Install the latest Contrast Coordinator release, comprising a single replica deployment and a LoadBalancer service, into your cluster.

kubectl apply -f https://github.com/edgelesssys/contrast/releases/download/v1.5.1/coordinator-aks-clh-snp.yml

Prepare your Kubernetes resources​

Your Kubernetes resources need some modifications to run as Confidential Containers. This section guides you through the process and outlines the necessary changes.

Security review​

Contrast ensures integrity and confidentiality of the applications, but interactions with untrusted systems require the developers' attention. Review the security considerations and the certificates section for writing secure Contrast application.

RuntimeClass​

Contrast will add annotations to your Kubernetes YAML files. If you want to keep the original files unchanged, you can copy the files into a separate local directory. You can also generate files from a Helm chart or from a Kustomization.

mkdir resources
kustomize build $MY_RESOURCE_DIR > resources/all.yml

To specify that a workload (pod, deployment, etc.) should be deployed as confidential containers, add runtimeClassName: contrast-cc to the pod spec (pod definition or template). This is a placeholder name that will be replaced by a versioned runtimeClassName when generating policies.

spec: # v1.PodSpec
  runtimeClassName: contrast-cc

Pod resources​

Contrast workloads are deployed as one confidential virtual machine (CVM) per pod. In order to configure the CVM resources correctly, Contrast workloads require a stricter specification of pod resources compared to standard Kubernetes resource management.

The total memory available to the CVM is calculated from the sum of the individual containers' memory limits and a static RuntimeClass overhead that accounts for services running inside the CVM. Consider the following abbreviated example resource definitions:

kind: RuntimeClass
handler: contrast-cc
overhead:
  podFixed:
    memory: 256Mi
---
spec: # v1.PodSpec
  containers:
  - name: my-container
    image: "my-image@sha256:..."
    resources:
      limits:
        memory: 128Mi
  - name: my-sidecar
    image: "my-other-image@sha256:..."
    resources:
      limits:
        memory: 64Mi

Contrast launches this pod as a VM with 448MiB of memory: 192MiB for the containers and 256MiB for the Linux kernel, the Kata agent and other base processes.

When calculating the VM resource requirements, init containers aren't taken into account. If you have an init container that requires large amounts of memory, you need to adjust the memory limit of one of the main containers in the pod. Since memory can't be shared dynamically with the host, each container should have a memory limit that covers its worst-case requirements.

Kubernetes packs a node until the sum of pod requests reaches the node's total memory. Since a Contrast pod is always going to consume node memory according to the limits, the accounting is only correct if the request is equal to the limit. Thus, once you determined the memory requirements of your application, you should add a resource section to the pod specification with request and limit:

spec: # v1.PodSpec
  containers:
  - name: my-container
    image: "my-image@sha256:..."
    resources:
      requests:
        memory: 50Mi
      limits:
        memory: 50Mi
note

On bare metal platforms, container images are pulled from within the guest CVM and stored in encrypted memory. The CVM mounts a tmpfs for the image layers that's capped at 50% of the total VM memory. This tmpfs holds the extracted image layers, so the uncompressed image size needs to be taken into account when setting the container limits. Registry interfaces often show the compressed size of an image, the decompressed image is usually a factor of 2-4x larger if the content is mostly binary. For example, the nginx:stable image reports a compressed image size of 67MiB, but storing the uncompressed layers needs about 184MiB of memory. Although only the extracted layers are stored, and those layers are reused across containers within the same pod, the memory limit should account for both the compressed and the decompressed layer simultaneously. Altogether, setting the limit to 10x the compressed image size should be sufficient for small to medium images.

Handling TLS​

In the initialization process, the contrast-secrets shared volume is populated with X.509 certificates for your workload. These certificates are used by the Contrast Service Mesh, but can also be used by your application directly. The following tab group explains the setup for both scenarios.

Contrast can be configured to handle TLS in a sidecar container. This is useful for workloads that are hard to configure with custom certificates, like Java applications.

Configuration of the sidecar depends heavily on the application. The following example is for an application with these properties:

  • The container has a main application at TCP port 8001, which should be TLS-wrapped and doesn't require client authentication.
  • The container has a metrics endpoint at TCP port 8080, which should be accessible in plain text.
  • All other endpoints require client authentication.
  • The app connects to a Kubernetes service backend.default:4001, which requires client authentication.

Add the following annotations to your workload:

metadata: # apps/v1.Deployment, apps/v1.DaemonSet, ...
  annotations:
    contrast.edgeless.systems/servicemesh-ingress: "main#8001#false##metrics#8080#true"
    contrast.edgeless.systems/servicemesh-egress: "backend#127.0.0.2:4001#backend.default:4001"

During the generate step, this configuration will be translated into a Service Mesh sidecar container which handles TLS connections automatically. The only change required to the app itself is to let it connect to 127.0.0.2:4001 to reach the backend service. You can find more detailed documentation in the Service Mesh chapter.

Using GPUs​

If the cluster is configured for GPU usage, Pods can use GPU devices if needed.

To do so, a CDI annotation needs to be added, specifying to use the pgpu (passthrough GPU) mode. The 0 corresponds to the PCI device index.

  • For nodes with a single GPU, this value is always 0.
  • For nodes with multiple GPUs, the value needs to correspond to the device's order as enumerated on the PCI bus. You can identify this order by inspecting the /var/run/cdi/nvidia.com-pgpu.yaml file on the specific node.

This process ensures the correct GPU is allocated to the workload.

As the footprint of a GPU-enabled pod-VM is larger than one of a non-GPU one, the memory of the pod-VM can be adjusted by using the io.katacontainers.config.hypervisor.default_memory annotation, which receives the memory the VM should receive in MiB. The example below sets it to 16 GB. A reasonable minimum for a GPU pod with a light workload is 8 GB.

metadata:
  # ...
  annotations:
    # ...
    cdi.k8s.io/gpu: "nvidia.com/pgpu=0"
    io.katacontainers.config.hypervisor.default_memory: "16384"

In addition, the container within the pod that requires GPU access must include a device request. This request specifies the number of GPUs the container should use. The identifiers for the GPUs, obtained during the deployment of the NVIDIA GPU Operator, must be included in the request. In the provided example, the container is allocated a single NVIDIA H100 GPU.

Finally, the environment variable NVIDIA_VISIBLE_DEVICES must be set to all to grant the container access to GPU utilities provided by the pod-VM. This includes essential tools like CUDA libraries, which are required for running GPU workloads.

spec:
  # ...
  containers:
  - # ...
    resources:
      limits:
        "nvidia.com/GH100_H100_PCIE": 1
    env:
    # ...
    - name: NVIDIA_VISIBLE_DEVICES
      value: all
note

A pod configured to use GPU support may take a few minutes to come up, as the VM creation and boot procedure needs to do more work compared to a non-GPU pod.

Generate policy annotations and manifest​

Run the generate command to add the necessary components to your deployment files. This will add the Contrast Initializer to every workload with the specified contrast-cc runtime class and the Contrast Service Mesh to all workloads that have a specified configuration. After that, it will generate the execution policies and add them as annotations to your deployment files. A manifest.json with the reference values of your deployment will be created.

contrast generate --reference-values aks-clh-snp resources/
warning

Please be aware that runtime policies currently have some blind spots. For example, they can't guarantee the starting order of containers. See the current limitations for more details.

Running contrast generate for the first time creates some additional files in the working directory:

  • seedshare-owner.pem is required for handling the secret seed and recovering the Coordinator (see Secrets & recovery).
  • workload-owner.pem is required for manifest updates after the initial contrast set.
  • rules.rego and settings.json are the basis for runtime policies.
  • layers-cache.json caches container image layer information for your deployments to speed up subsequent runs of contrast generate.

If you don't want the Contrast Initializer to automatically be added to your workloads, there are two ways you can skip the Initializer injection step, depending on how you want to customize your deployment.

You can disable the Initializer injection completely by specifying the --skip-initializer flag in the generate command.

contrast generate --reference-values aks-clh-snp --skip-initializer resources/

When disabling the automatic Initializer injection, you can manually add the Initializer as a sidecar container to your workload before generating the policies. Configure the workload to use the certificates written to the contrast-secrets volumeMount.

# v1.PodSpec
spec:
  initContainers:
    - env:
        - name: COORDINATOR_HOST
          value: coordinator
      image: "ghcr.io/edgelesssys/contrast/initializer:v1.5.1@sha256:6663c11ee05b77870572279d433fe24dc5ef6490392ee29a923243cfc40f2f35"
      name: contrast-initializer
      volumeMounts:
        - mountPath: /contrast
          name: contrast-secrets
  volumes:
    - emptyDir: {}
      name: contrast-secrets

Apply the resources​

Apply the resources to the cluster. Your workloads will block in the initialization phase until a manifest is set at the Coordinator.

kubectl apply -f resources/

Connect to the Contrast Coordinator​

For the next steps, we will need to connect to the Coordinator. The released Coordinator resource includes a LoadBalancer definition we can use.

coordinator=$(kubectl get svc coordinator -o=jsonpath='{.status.loadBalancer.ingress[0].ip}')
Port-forwarding of Confidential Containers

kubectl port-forward uses a Container Runtime Interface (CRI) method that isn't supported by the Kata shim. If you can't use a public load balancer, you can deploy a port-forwarder. The port-forwarder relays traffic from a CoCo pod and can be accessed via kubectl port-forward.

Upstream tracking issue: https://github.com/kata-containers/kata-containers/issues/1693.

Set the manifest​

Attest the Coordinator and set the manifest:

contrast set -c "${coordinator}:1313" resources/

This will use the reference values from the manifest file to attest the Coordinator. After this step, the Coordinator will start issuing TLS certificates to the workloads. The init container will fetch a certificate for the workload and the workload is started.

warning

On bare metal, the coordinator policy hash must be overwritten using --coordinator-policy-hash.

Verify the Coordinator​

An end user (data owner) can verify the Contrast deployment using the verify command.

contrast verify -c "${coordinator}:1313"

The CLI will attest the Coordinator using the reference values from the given manifest file. It will then write the service mesh root certificate and the history of manifests into the verify/ directory. In addition, the policies referenced in the active manifest are also written to the directory. The verification will fail if the active manifest at the Coordinator doesn't match the manifest passed to the CLI.

warning

On bare metal, the coordinator policy hash must be overwritten using --coordinator-policy-hash.

Communicate with workloads​

You can securely connect to the workloads using the Coordinator's mesh-ca.pem as a trusted CA certificate. First, expose the service on a public IP address via a LoadBalancer service:

kubectl patch svc ${MY_SERVICE} -p '{"spec": {"type": "LoadBalancer"}}'
kubectl wait --timeout=30s --for=jsonpath='{.status.loadBalancer.ingress}' service/${MY_SERVICE}
lbip=$(kubectl get svc ${MY_SERVICE} -o=jsonpath='{.status.loadBalancer.ingress[0].ip}')
echo $lbip
Subject alternative names and LoadBalancer IP

By default, mesh certificates are issued with a wildcard DNS entry. The web frontend is accessed via load balancer IP in this demo. Tools like curl check the certificate for IP entries in the SAN field. Validation fails since the certificate contains no IP entries as a subject alternative name (SAN). For example, attempting to connect with curl and the mesh CA certificate will throw the following error:

$ curl --cacert ./verify/mesh-ca.pem "https://${frontendIP}:443"
curl: (60) SSL: no alternative certificate subject name matches target host name '203.0.113.34'

Using openssl, the certificate of the service can be validated with the mesh-ca.pem:

openssl s_client -CAfile verify/mesh-ca.pem -verify_return_error -connect ${frontendIP}:443 < /dev/null

Recover the Coordinator​

If the Contrast Coordinator restarts, it enters recovery mode and waits for an operator to provide key material. For demonstration purposes, you can simulate this scenario by deleting the Coordinator pod.

kubectl delete pod -l app.kubernetes.io/name=coordinator

Kubernetes schedules a new pod, but that pod doesn't have access to the key material the previous pod held in memory and can't issue certificates for workloads yet. You can confirm this by running verify again, or you can restart a workload pod, which should stay in the initialization phase. However, you can recover the Coordinator using the secret seed and the seed share owner key in your working directory.

contrast recover -c "${coordinator}:1313"

Now that the Coordinator is recovered, all workloads should pass initialization and enter the running state. You can now verify the Coordinator again, which should return the same manifest you set before.

warning

The recovery process invalidates the mesh CA certificate: existing workloads won't be able to communicate with workloads newly spawned. All workloads should be restarted after the recovery succeeded.

warning

On bare metal, the coordinator policy hash must be overwritten using --coordinator-policy-hash.