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curl -k -X PUT "https://AAI:AAI@<worker-vmIp>:30233/aai/v23/external-system/esr-thirdparty-sdnc-list/esr-thirdparty-sdnc/ff9ef162-951d-4e14-9ce6-b4fa0adf896b" \
-H 'Accept: application/json' \
-H 'X-FromAppId: AAI' \
-H 'X-TransactionId: 1' \
-H 'Content-Type: application/json' \
-d '{
"thirdparty-sdnc-id":"ff9ef162-951d-4e14-9ce6-b4fa0adf896b",
    "location": "edge",
    "product-name": "TSDN",
	 "esr-system-info-list":{"esr-system-info":[{
"esr-system-info-id": "7c29b9df-feef-4fa7-b56d-3e39f5ef4a90",
"system-name": "sdnc2",
"vendor": "HUAWEI",
"type": "WAN",
"version": "v1.0",
"service-url": "http://<simulator-ip>:<simulator-port>",
"user-name": "onos",
"password": "rocks",
"system-type": "thirdparty_SDNC",
"protocol": "restconf"
}]}
}' 

E2E network slicing test(using resource occupancy details)

Before creating shared communication service for testing NSI/NSSI selection using resource occupancy details, below manual configurations must be done:

Deployment Prerequisite/dependencies

1. VES, PM mapper and DataFile Collector, dcae-mongodb, Datalake Extraction Service(DES) and Datalake-feeder services should be running.

Datalake Extraction Service(DES):

1. Build presto image and push the images to a exsting repository
   1. Download and extract presto package version v0.0.2: presto-v0.0.2.tar.gz
   2. docker build -t presto:v0.0.2 .
   3. docker tag presto:v0.0.2 registry.baidubce.com/onap/presto:v0.0.2
   4. docker push registry.baidubce.com/onap/presto:v0.0.2
   5. Note： Replace the repository path with your own repository.
2. Install presto service
   1. kubectl -n onap run dl-presto --image=presto:v0.0.2 --env="MongoDB_IP=dcae-mongohost" --env="MongoDB_PORT=27017" --image-pull-policy=IfNotPresent
   2. kubectl expose pod dl-presto --name=dl-presto --port=9000 --target-port=9000 --type=NodePort -n onap
3. Login to the dev-dcae-datalake-postgres-primary container and execute following sql commands:

   1. 
      

      Code Block
      language text title esr_registration linenumbers true collapse true
      delete from map_db_topic; delete from map_db_design; delete from db; delete from data_exposure ; insert into db (id, db_type_id, enabled, encrypt, name,host,port,database_name) values (2, 'MONGO', true, true, 'MongoDB 1','dcae-mongohost',27017,'datalake'); insert into map_db_topic(db_id,topic_id) select db.id, topic.id from db_type, db, topic where db.db_type_id=db_type.id and db_type.tool=false; update kafka set secure=true, pass='admin_secret' where id=1; insert into topic_name (id) values ('unauthenticated.PERFORMANCE_MEASUREMENTS'); insert into topic(id, topic_name_id,correlate_cleared_message,enabled, message_id_path,data_format) values (6, 'unauthenticated.PERFORMANCE_MEASUREMENTS',true,true,'/event/commonEventHeader','JSON'); insert into data_exposure(id, note,sql_template,db_id) values ('pm_data','pm_message', 'SELECT json_format(CAST( event.perf3gppFields.measDataCollection.measInfoList[1].measTypes.sMeasTypesList AS JSON)) AS sMeasTypesList, json_format(CAST( event.perf3gppFields.measDataCollection.measInfoList[1].measValuesList AS JSON)) AS measValuesList FROM unauthenticatedperformancemeasurements WHERE any_match(event.perf3gppFields.measDataCollection.measInfoList[1].measTypes.sMeasTypesList, e -> e like ''${snssai}'') AND event.commonEventHeader.startEpochMicrosec >= ${time}',2); //db_id refers to mongodb in table db, this template filters PM data based on snssai and startEpochMicrosec;

      
      

   2. Load sample data in mongo db(refer DCAE mongodb section)

   3. Execute below request to verify: curl --header "Content-type: application/json" --request POST --data '{"snssai":"SM.PrbUsedDl.01-910EF6","time":"1649853949111"}' http://dl-des:1681/datalake/v1/exposure/pm_data -i

      
      

Datalake Feeder:

1. Restart datalake-feeder container after loading above configurations in the DB.

RANSIM :

1. RANSIM and honeycomb containers should be running
2. You can update/add/delete data(throughput, PRBs, maxNoConns etc) in mariadb container(credentials in docker-compose file) if required before generation of PM data.
3. RANSIM must contain 'plmninfo' of the service which is to be shared with other services. Then generate closed-loop PM data which will be stored in mongo-db by datalake-feeder.
4. Sample closed loop data: closed-loop-data.txt
5. Run the following command to generate Closed Loop PM data:
   1. curl -X POST http://localhost:8081/ransim/api/generateClosedLoopPmData -i
6. Run the following command to stop Closed Loop PM data(When enough PM data is generated): 
   1. curl -X GET http://localhost:8081/ransim/api/stopClosedLoopPmData -i
7. Access the DU honeycomb containers to view the logs:
   1. docker container logs -f 110
8. Check the folder(where SFTP setup is done) to view the generated PM data files.
9. Access VES, data file collector, dmaap-dr, PM mapper and slice-analysis logs to verify.

DCAE-MONGO DB:

1. Login to dcae-mongodb container and execute following commands to check if PM data was saved in DB:
   1. mongo
   2. use datalake
   3. db.unauthenticatedperformancemeasurements.find({})
   4. to load sample data:

   5. 
      

      Code Block
      language text title esr_registration linenumbers true collapse true
      db.unauthenticatedperformancemeasurements.insert({ "event":{ "commonEventHeader":{ "domain":"perf3gpp", "eventId":"95e79282-2876-4000-8192-b514ad7f399a", "sequence":0, "eventName":"perf3gpp_RnNode-Slicing_pmMeasResult", "sourceName":"220", "reportingEntityName":"", "priority":"Normal", "startEpochMicrosec":1651216762867, "lastEpochMicrosec":1651216762868, "version":"4.0", "vesEventListenerVersion":"7.1", "timeZoneOffset":"UTC+05:30" }, "perf3gppFields":{ "perf3gppFieldsVersion":"1.0", "measDataCollection":{ "granularityPeriod":900, "measuredEntityUserName":"", "measuredEntityDn":"220", "measuredEntitySoftwareVersion":"r0.1", "measInfoList":[ { "measInfoId":{ "sMeasInfoId":"measInfoIsVal" }, "measTypes":{ "sMeasTypesList":[ "SM.PrbUsedDl.01-3C5C90", "SM.PrbUsedUl.01-3C5C90" ] }, "measValuesList":[ { "measObjInstId":"15155", "suspectFlag":"false", "measResults":[ { "p":1, "sValue":"5" }, { "p":2, "sValue":"7" } ] }, { "measObjInstId":"15174", "suspectFlag":"false", "measResults":[ { "p":1, "sValue":"6" }, { "p":2, "sValue":"7" } ] }, { "measObjInstId":"15175", "suspectFlag":"false", "measResults":[ { "p":1, "sValue":"15" }, { "p":2, "sValue":"4" } ] }, { "measObjInstId":"15176", "suspectFlag":"false", "measResults":[ { "p":1, "sValue":"5" }, { "p":2, "sValue":"14" } ] }, { "measObjInstId":"15825", "suspectFlag":"false", "measResults":[ { "p":1, "sValue":"0" }, { "p":2, "sValue":"14" } ] } ] } ] } } } })