Follow the instructions below to deploy the Multi-Cluster Application Dispatcher (MCAD) controller in an existing Kubernetes cluster:
# kubectl version --short=true
Client Version: v1.11.9
Server Version: v1.11.9
#
# kubectl get pods -n kube-system
#
Install the Helm Client on your local machine and the Helm Cerver on your kubernetes cluster. Helm installation documentation is [here] (https://docs.helm.sh/using_helm/#installing-helm). After you install Helm you can list the Help packages installed with the following command:
# helm list
#
Follow the build instructions here to build the multi-cluster-app-dispatcher controller docker image and push the image to a docker registry.
The default memory resource demand for the multi-cluster-app-dispatcher controller is 2Gig. If your cluster is a small installation such as MiniKube you will want to adjust the Helm installation resource requests for the MCAD controller accordingly.
To list available compute nodes on your cluster enter the following command:
kubectl get nodes
For example:
$ kubectl get nodes
NAME STATUS ROLES AGE VERSION
minikube Ready master 91d v1.10.0
To find out the available resources in you cluster inspect each node from the command output above with the following command:
$ kubectl describe node <node_name>
For example:
$ kubectl describe node minikube
...
Name: minikube
Roles: master
Labels: beta.kubernetes.io/arch=amd64
beta.kubernetes.io/os=linux
...
Capacity:
cpu: 2
ephemeral-storage: 16888216Ki
hugepages-2Mi: 0
memory: 2038624Ki
pods: 110
Allocatable:
cpu: 2
ephemeral-storage: 15564179840
hugepages-2Mi: 0
memory: 1936224Ki
pods: 110
...
Allocated resources:
(Total limits may be over 100 percent, i.e., overcommitted.)
Resource Requests Limits
-------- -------- ------
cpu 1915m (95%) 1 (50%)
memory 1254Mi (66%) 1364Mi (72%)
Events: <none>
In the example above, there is only one node (minikube) in the cluster with the majority of the cluster memory used (1,254Mi) out of 1,936Mi allocatable capacity) leaving less than 700Mi available capacity for new pod deployments in the cluster. Since the default memory demand for the Multi-Cluster Application Dispatcher controller pod is 2Gig the cluster has insufficient memory to deploy the controller. Instruction notes provided below in Example 3 shows how to adjust the resource definitions using the Helm parameters to fit in the available capacity in your cluster.
# git clone https://github.com/IBM/multi-cluster-app-dispatcher.git
#or
# git clone git@github.com:IBM/multi-cluster-app-dispatcher.git
#
cd multi-cluster-app-wrapper/deployment
Install the Multi-Cluster-App-Dispatcher Controller using the commands below. The --wait parameter in the Helm command below is used to ensure all pods of the helm chart are running and will not return unless the default timeout expires (typically 300 seconds) or all the pods are in Running state.
Before submitting the command below you should ensure you have enough resources in your cluster to deploy the helm chart (see Pre-Reqs section above). If you do not have enough compute resources in your cluster to run with the default allocation, you can adjust the resource request via the command line by using the optional parameters --resources.*.*. See an example Example 3 in section 3.a. below.
All Helm parameters are described in the table at the bottom of this section.
3.a) Start the Multi-Cluster-App-Dispatcher Controller on All Target Deployment Clusters (Agent Mode).
Agent Mode: Install and set up the multi-cluster-app-dispatcher controller (MCAD) in Agent Mode for each clusters that will orchestrate the resources defined within an AppWrapper using Helm. Agent Mode is the default mode when deploying the MCAD controller.
helm install mcad-controller --namespace kube-system --wait --set image.repository=<image repository and name> --set image.tag=<image tag> --set imagePullSecret.name=<Name of image pull kubernetes secret> --set imagePullSecret.password=<REPLACE_WITH_REGISTRY_TOKEN_GENERATED_IN_PREREQs_STAGE1_REGISTRY.d)> --set localConfigName=<Local Kubernetes Config File for Current Cluster> --set volumes.hostPath=<Host_Path_location_of_local_Kubernetes_config_file>
Assuming the default for image.repository and image.tag fields:
helm install mcad-controller --namespace kube-system
Assuming the MCAD controller image is already pulled onto the local target machine with the following image image.repository=mcad-controller, image.tag=latest
helm install mcad-controller --namespace kube-system --wait --set image.pullPolicy=Never --set image.repository=mcad-controller --set image.tag=latest
To adjust the cpu and memory demands of the deployment with command line overrides example:
helm install mcad-controller --namespace kube-system --wait --set resources.requests.cpu=1000m --set resources.requests.memory=1024Mi --set resources.limits.cpu=1000m --set resources.limits.memory=1024Mi --set image.repository=myDockerReegistry/mcad-controller --set image.tag=latest --set image.pullPolicy=Always
Dispatcher Mode_: Install and set up the Multi-Cluster-App-Dispatcher Controler (MCAD) in Dispatcher Mode for the control cluster that will dispatch the MCAD controller to an Agent cluster using Helm.
Dispatcher Mode: Installing the Multi-Cluster-App-Dispatcher Controler in Dispatcher Mode.
helm install mcad-controller --namespace kube-system --wait --set image.repository=<image repository and name> --set image.tag=<image tag> --set configMap.name=<Config> --set configMap.dispatcherMode='"true"' --set configMap.agentConfigs=agent101config:uncordon --set volumes.hostPath=<Host_Path_location_of_all_agent_Kubernetes_config_files>
For example:
helm install mcad-controller --namespace kube-system --wait --set image.repository=tonghoon --set image.tag=both --set configMap.name=mcad-deployer --set configMap.dispatcherMode='"true"' --set configMap.agentConfigs=agent101config:uncordon --set volumes.hostPath=/etc/kubernetes
The following table lists the configurable parameters of the helm chart and their default values.
| Parameter | Description | Default | Sample values |
|---|---|---|---|
configMap.agentConfigs |
For Every Agent Cluster separated by commas(,): Name of agent config file : Set the dispatching mode for the Agent Cluster. Note:For the dispatching mode uncordon, indicating MCAD controller is allowed to dispatched jobs to the Agent Cluster, is only supported. |
<No default for agent config file>:uncordon |
agent101config:uncordon,agent110config:uncordon |
configMap.dispatcherMode |
Whether the MCAD Controller should be launched in Dispatcher mode or not | false |
true |
configMap.name |
Name of the Kubernetes ConfigMap resource to configure the MCAD Controller | mcad-deployer |
|
deploymentName |
Name of MCAD Controller Deployment Object | mcad-controller |
my-mcad-controller |
image.pullPolicy |
Policy that dictates when the specified image is pulled | Always |
Never |
imagePullSecret.name |
Kubernetes secret name to store password for image registry | mcad-controller-registry-secret |
|
imagePullSecret.password |
Image registry pull secret password | eyJhbGc...y8gJNcpnipUu0 |
|
imagePullSecret.username |
Image registry pull user name | iamapikey |
token |
image.repository |
Name of repository containing MCAD Controller image | registry.stage1.ng.bluemix.net/ibm/mcad-controller |
my-repository |
image.tag |
Tag of desired image within repository | latest |
my-image |
namespace |
Namespace in which MCAD Controller Deployment is created | kube-system |
my-namespace |
nodeSelector.hostname |
Host Name field for MCAD Controller Pod Node Selector | example-host |
|
replicaCount |
Number of replicas of MCAD Controller Deployment | 1 | 2 |
resources.limits.cpu |
CPU Limit for MCAD Controller Deployment | 2000m |
1000m |
resources.limits.memory |
Memory Limit for MCAD Controller Deployment | 2048Mi |
1024Mi |
resources.requests.cpu |
CPU Request for MCAD Controller Deployment (must be less than CPU Limit) | 2000m |
1000m |
resources.requests.memory |
Memory Request for MCAD Controller Deployment (must be less than Memory Limit) | 2048Mi |
1024Mi |
serviceAccount |
Name of service account of MCAD Controller | mcad-controller |
my-service-account |
volumes.hostPath |
Full path on the host location where the localConfigName file is stored |
/etc/kubernetes |
List the Helm installation. The STATUS should be DEPLOYED.
NOTE: The --wait parameter in the helm installation command from Step 3 above ensures all resources are deployed and running if the STATUS indicates DEPLOYED. Installing the Helm Chart without the --wait parameter does not ensure all resources are successfully running but may still show a Status of Deployed.
The STATUS value of FAILED indicates all resources were not created and running before the timeout occurred. Usually this indicates a pod creation failure is due to insufficient resources to create the Multi-Cluster-App-Dispatcher Controller pod. Example instructions on how to adjust the resources requested for the Helm chart are described in the NOTE comment of step #4 above.
$ helm list
NAME REVISION UPDATED STATUS CHART NAMESPACE
opinionated-antelope 1 Mon Jan 21 00:52:39 2019 DEPLOYED mcad-controller-0.1.0 kube-system
Ensure the new custom resource is enabled by listing the appwrappeer jobs.
$ kubectl get appwrappers
No resources found in default namespace.
$Since no appwrapper jobs have yet to be deployed into the current cluster you should receive a message indicating No resources found for appwrappers but your cluster now has MCAD controller enabled. Use the tutorial to deploy an example appwrapper job.
List the deployed Helm charts and identify the name of the Multi-Cluster-App-Dispatcher Controller installation.
helm listFor Example
$ helm list
NAME REVISION UPDATED STATUS CHART NAMESPACE
opinionated-antelope 1 Mon Jan 21 00:52:39 2019 DEPLOYED mcad-controller-0.1.0 kube-system
Delete the Helm deployment.
helm delete <deployment_name>
For example:
helm delete opinionated-antelope