Common Workflow Scheduler for Kubernetes

In this repository, you will find the Common Workflow Scheduler for Kubernetes proposed in the paper "How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface."

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Build

docker build -t cws .
docker tag cws <your docker account>/cws:<version>
docker push <your docker account>/cws:<version>

API Endpoints

#	Resource	Method
1	/{version}/{execution}	POST
2	/{version}/{execution}	DELETE
3	/{version}/{execution}/DAG/vertices	POST
4	/{version}/{execution}/DAG/vertices	DELETE
5	/{version}/{execution}/DAG/edges	POST
6	/{version}/{execution}/DAG/edges	DELETE
7	/{version}/{execution}/startBatch	PUT
8	/{version}/{execution}/endBatch	PUT
9	/{version}/{execution}/task/{id}	POST
10	/{version}/{execution}/task/{id}	GET
11	/{version}/{execution}/task/{id}	DELETE

SWAGGER: /swagger-ui.html
API-DOCS: /v3/api-docs

For more details, we refer to the paper.

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Run on Kubernetes

You need to create an account to use the Common Workflow Scheduler in Kubernetes. Therefore, create a file account.yaml with the following content. Afterward, apply this to your Kubernetes cluster kubectl apply -f account.yaml

apiVersion: v1
kind: ServiceAccount
metadata:
  name: cwsaccount

---

kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: cwsrole
rules:
 - apiGroups: [""]
   resources: ["pods","pods/status","pods/exec","nodes","bindings","configmaps"]
   verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
 - apiGroups: ["metrics.k8s.io"]
   resources: ["nodes"]
   verbs: ["get","list"]

---

kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
   name: cwsbinding
subjects:
 - kind: ServiceAccount
   name: cwsaccount
   namespace: <your namespace>
roleRef:
   kind: ClusterRole
   name: cwsrole
   apiGroup: rbac.authorization.k8s.io

Next, you can start the Common Workflow Scheduler in your Kubernetes environment. Nextflow will start the Common Workflow Scheduler automatically. Therefore, create a file cws.yaml with the following content. Afterward, apply this to your Kubernetes cluster kubectl apply -f cws.yaml

apiVersion: v1
kind: Pod
metadata:
  labels:
    app: cws
    component: scheduler
    tier: control-plane
  name: workflow-scheduler
spec:
  containers:
  - env:
    - name: SCHEDULER_NAME
      value: workflow-scheduler
    - name: AUTOCLOSE
      value: "false"
    image: commonworkflowscheduler/kubernetesscheduler:v1.0
    imagePullPolicy: Always
    name: workflow-scheduler
    resources:
      limits:
        cpu: "2"
        memory: 1400Mi
      requests:
        cpu: "2"
        memory: 1400Mi
    volumeMounts:
    - mountPath: /input # mount at the same path as you do in your workflow
      name: vol-1
    - mountPath: /data
      name: vol-2
  securityContext:
    runAsUser: 0
  serviceAccount: cwsaccount # use the account created before
  volumes:
  - name: vol-1
    persistentVolumeClaim:
      claimName: api-exp-input # mount the same pvc as you use in your workflow.
  - name: vol-2
    persistentVolumeClaim:
      claimName: api-exp-data

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If you use this software or artifacts in a publication, please cite it as:

Text

Lehmann Fabian, Jonathan Bader, Friedrich Tschirpke, Lauritz Thamsen, and Ulf Leser. How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface. In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid). Bangalore, India, 2023.

BibTeX

@inproceedings{lehmannHowWorkflowEngines2023,
 author = {Lehmann, Fabian and Bader, Jonathan and Tschirpke, Friedrich and Thamsen, Lauritz and Leser, Ulf},
 booktitle = {2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)},
 title = {How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface},
 year = {2023},
 address = {{Bangalore, India}},
 doi = {10.1109/CCGrid57682.2023.00025}
}

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Acknowledgement:

This work was funded by the German Research Foundation (DFG), CRC 1404: "FONDA: Foundations of Workflows for Large-Scale Scientific Data Analysis."