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- Configuration of E2E network slices (~ 5 slices)
- RAN Simulator, VES Collector, PM Mapper, Data File Collector, Slice Analysis MS, ML Prediction MS, SO, Policy, SDNR, Config DB components should be up and running
- SFTP setup to store the PM messages from RAN Simulator
- Manual configurations are detailed at Closed Loop for Network Slicing
- To Train and predict Machine learning model, following ML frameworks are used Tensorflow, Keras and sklearn.
Functionality of ML block is divided into two parts.
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Step2: Prediction
The prediction is performed is performed by first by acquiring the current time instance data from slices and cells. The data are acquired from following APIs.
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- The Prediction module start to get the acquired data for all the slices and cells using the PM Mapper topic
At start of the prediction process, we have a cold start condition for the model to make accurate predictions, The actual prediction starting from the 5th time instance, while each time instance data are generated for every 15 minutes, the 5th time instance data will be retrieved after 1 hour 15 minutes, till then the model generated synthetic data for the first 4 time instant we are generating and then performs prediction. This is done as a softmax correction, essential to get better accuracy. After the first 4 time instance the predicted values are used and and taken forward.
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curl -X POST -H "Content-Type: text/plain" https://localhost:8081/events/org.onap.dmaap.mr.PERFORMANCE_MEASUREMENTS/mlms-cg/mlms-cid |
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