Page Comparison

1. UUI Configuration

...

Copy subnetCapability.json to SO-API Handler pod to configure subnet capabilities at run time.

Sample subnetCapability.json

{
"AN_NF": {
"latency": 5,
"maxNumberofUEs": 200,
"maxThroughput": 90,
"termDensity": 40
},
"AN": {
"latency": 20,
"maxNumberofUEs": 100,
"maxThroughput": 150,
"termDensity": 50
},
"CN": {
"latency": 10,
"maxThroughput": 50,
"maxNumberofConns": 100
},
"TN_FH": {
"latency": 10,
"maxThroughput": 90
},
"TN_MH": {
"latency": 5,
"maxThroughput": 90
},
"TN_BH": {
"latency": 10,
"maxThroughput": 100
}
}

...

Code Block

language	text
title	esr_registration
linenumbers	true
collapse	true

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

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

Datalake Extraction Service(DES):

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.
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
Login to the dev-dcae-datalake-postgres-primary container and execute following sql commands:
1. Code Block
  language text
  title datalake

...

-db init_data
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;

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

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

RANSIM :

RANSIM and honeycomb containers should be running
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.
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.
Sample closed loop data: closed-loop-data.txt
Run the following command to generate Closed Loop PM data:
1. curl -X POST http://localhost:8081/ransim/api/generateClosedLoopPmData -i
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
Access the DU honeycomb containers to view the logs:
1. docker container logs -f 110
Check the folder(where SFTP setup is done) to view the generated PM data files.
Access VES, data file collector, dmaap-dr, PM mapper and slice-analysis logs to verify.

DCAE-MONGO DB:

Login to dcae-mongodb container and execute following commands to check if PM data was saved in DB:

mongo
use datalake
db.unauthenticatedperformancemeasurements.find({})
to load sample data: