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Manual Configuration:

This configuration is required to enable the ranSlice features in SDN-R.

SDNC:

•  Create the below environment variable in the helm chart at kubernetes/sdnc/templates/statefulset.yaml (for sdnc container). 

+          - name: SDNR_NORTHBOUND
+            value: "{{ .Values.config.sdnr.enabled | default "false"}}"

• Make the SDNC chart and then deploy.
• Execute the following command to log into the sdnc container 
  1. kubectl exec -it dev-sdnc-0 bash 
• Once in the docker container, edit the properties file  /opt/onap/ccsdk/data/properties/ran-slice-api-dg.properties
  •  update the following flag to true.
    1. ransim-mounted=true
    2. configdb-deployed=false  (set to true if configDB is used)
    3. cps-deployed=true   (set to true if cps is used)
  • Update IP address for ConfigDB:
    1. configdb.url=${CONFIG_DB_URL:-http://<IPAddress>:8086/api/sdnc-config-db/v4}
  • Update IP address for CPS-TBDMT:
    1. restapi.cps.anchors.endpoint=${CPS_URL:-http://<IPAddress>:8883}/cps/api/v1/dataspaces/E2EDemo/anchors

POLICY:

• Login to the policy-drools-pdp container and check if the value 'actor.service.SO.operations.Modify\ NSSI.path=3gppservices/v1/modify' is present in the below file:
  • kubectl exec -ti -n onap dev-policy-drools-pdp-0 sh
  • /opt/app/policy/config/event-manager.properties
• If value not present update the policy-drools-pdp-image to this version'/onap/policy-pdpd-cl:1.10.2-20220413T1305' or any version after '1.10.2'

Policies needed:

Execute these curl requests from any of the running pods.

...

where modifynssi.json is, 

To push the policy:

curl --silent -k --user 'policyadmin:zb!XztG34' -X POST "https://policy-pap:6969/policy/pap/v1/pdps/policies" -H "Accept: application/json" -H "Content-Type: application/json" -d @push_modifynssi.json

where push_modifynssi.json is,

Deployment Prerequisite/dependencies

...

1. Download the configDb source files from configdb-3.0.0.zip
2. cd configdb (after extracting the files)
3. Setup and run maridb container
   1. sudo docker run -p 3306:3306 -v $PWD/SDNC_ConfigDB_SchemaV4.sql:/docker-entrypoint-initdb.d/SDNC_ConfigDB_SchemaV4.sql --name mariadb -e MYSQL_ROOT_PASSWORD=password -d mariadb
4. Navigate to project directory
   1. cd configdb/Config-DB-API-App
5. Build the application
   1. JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/ mvn clean install
6. Run the application
   1. JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/ mvn spring-boot:run &
7. Run this script to load sample data data config_requests_script_intelligent_slicing.shSample data to insert slice-profile configdb-data.txt

CPS and CPS-TBDMT:

CPS:

1. git clone "https://gerrit.onap.org/r/cps" 
2. cd cps/
3. mvn clean install
4. cd docker-compose/
5. docker-compose up

...

1. git clone "https://gerrit.onap.org/r/cps/cps-tbdmt" 
2. cd cps-tbdmt/
3. mvn clean install
4. cd docker-compose/
5. update cps & ncmp ip-address in application.yml
6. cps-tbdmt/docker-compose/application.yml should have the below mapping under the section schemaToAnchor:
   1. schemaToAnchor:
          ran-coverage-area: coverage-area-onap
          e2e-cavsta-schemaset: e2e-cavsta1
          CSIT-SchemaSet: CSIT-Anchor
          ns-schemaset: ns-anchor
          ran-network: 11
          ran-inventory: ran-inventory-anchor
7. docker-compose up (use the image built using the above steps in the docker-compose file)

...

Uploading cps sample data:

payload-ran-network.json
curl --location --user cpsuser:cpsr0cks! --request POST \
http://$CPS_IP:8080/cps/api/v1/dataspaces/E2EDemo/anchors/11/nodes \
--header 'Content-Type: application/json' \
-d @payload-ran-network.json

ran-inventory-sample-data.json

curl --location --user cpsuser:cpsr0cks! --request POST \
http://$CPS_IP:8080/cps/api/v1/dataspaces/E2EDemo/anchors/ran-inventory-anchor/nodes \
--header 'Content-Type: application/json' \
-d @ran-inventory-sample-data.json ran-inventory-sample-data.json 

Uploading tbdmt-templates:

...

      2. Give chmod 777 to upload folder which will be created automatically in the home dir.

ML-prediction-ms:

1. git clone "https://gerrit.onap.org/r/dcaegen2/services"(use patchset ml-prediction-ms if code not merged)
2. cd services/components/ml-prediction-ms
3. Update Ransim, Dmaap and ConfigDB IPAddress/Port details in ml-prediction-ms.config
4. Build docker image using: 'docker build -t ml-prediction-ms:latest .'
5. Run the ml-prediction-ms container.
   1. docker run -d --name ml-prediction-ms -p "5000:5000"  ml-prediction-ms:latest
6. To view the logs:
   1. docker exec -ti ml-prediction-ms bash
   2. tail -f IntelligentSliceMl.log

NOTE: Use sample data attached under CONFIGDB, RANSIM and CPS if E2E communication service is not done.