how to disable concurrency execution of batch job

[sugesh]

I'm currently using a GCP workflow for batch job processing, set to run every 30 minutes. However, I'm facing an issue where multiple jobs are sometimes created while old ones are still executing. How can I disable concurrency to ensure that only one job runs at a time? I want to avoid situations where new jobs are created while a job is still running. Any guidance on how to achieve this within the GCP workflow setup would be greatly appreciated.

 - createAndRunBatchJob:
                call: http.post
                args:
                  url: ##{batchApiUrl}
                  query:
                    job_id: ##{jobId}
                  headers:
                    Content-Type: application/json
                  auth:
                    type: OAuth2
                  body:
                    taskGroups:
                      taskSpec:
                        runnables:
                          - container:
                              imageUri: ##{imageUri}
                              commands:
                                - "--script-location"
                                - "/mnt/disks/batch-scripts-${project_sha}/google_tv_aep/batch/code"
                            environment:
                              variables:
                                job_id: ##{jobId}
                                secret: projects/${project}/secrets/${secret_nm}/versions/latest
                                local_path_1: /mnt/disks/${local_path_1}
                                local_path_2: /mnt/disks/${local_path_2}
                                query_file: "tv_mkt_campaign_events_load_query.sql"
                                output_dataset_name: "tv_mkt_campaign_events_incremental_data.json"
                                project: "${project}"
                                aep_sandbox: "${aep_sandbox}"
                                aep_dataset_name: "TV mkt campaign Events"
                                load_mode: "incremental"
                                aep_connection_id: "${aep_connection_id}"
                                checkpoint_file: "mkt_campaign_checkpoint.param"
                                aep_flow_id: "${aep_flow_id}"
                                checkpoint_field: timestamp
                        volumes:
                          - mountPath: /mnt/disks/batch-scripts-${project_sha}
                            gcs:
                              remotePath: batch-scripts-${project_sha}
                          - mountPath: /mnt/disks/${bucket_nm}
                            gcs:
                              remotePath: ${bucket_nm}
                        computeResource:
                          cpuMilli: 2000
                          memoryMib: 16384
                      taskCount: 1
                      parallelism: 2
                    allocationPolicy:
                      network:
                        networkInterfaces:
                          network: projects/${project}/global/networks/spark-network
                          subnetwork: projects/${project}/regions/${location}/subnetworks/spark-subnet-pr
                          noExternalIpAddress: true
                      serviceAccount:
                        email: ${builder}
                    logsPolicy:
                      destination: CLOUD_LOGGING
                result: createAndRunBatchJobResponse