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Refer Intelligent Slicing flow for complete closed loop sequence. Existing ML MS (not officially exposed to ONAP) requires certain enhancements to satisfy the intent guarantee.
Overview
![](/download/attachments/117743292/image2022-1-16_20-2-15.png?version=1&modificationDate=1642343533000&api=v2)
Functionality of ML block is divided into two parts.
- Model Training (Offline)
- Validation
- Prediction
Step1: ML Offline Training
This step requires a huge amount of historical data for the analysis. It requires both the input (PM metrics) and output data (intent).
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PM file format
PM file format for Training
<?xml version="1.0" encoding="UTF-8"?>
<measCollecFile xmlns="http://www.3gpp.org/ftp/specs/archive/32_series/32.435#measCollec">
<fileHeader dnPrefix="Prefix" fileFormatVersion="32.435 V10.0" vendorName="Acme Ltd">
<fileSender localDn="cucpserver1"/>
<measCollec beginTime="2020-10-14T14:39:20.469"/>
</fileHeader>
<measData>
<managedElement localDn="cucpserver1" swVersion="r0.1"/>
<measInfo measInfoId="measInfoIsVal">
<job jobId="1118"/>
<granPeriod duration="PT900S" endTime="2020-10-14T14:39:20.473"/>
<repPeriod duration="PT900S"/>
<measType p="1">SM.PDUSessionSetupReq.0011-0010</measType>
<measType p="2">SM.PDUSessionSetupSucc.0011-0010</measType>
<measType p="3">SM.PDUSessionSetupFail.0</measType>
<measType p="4">SM.PDUSessionSetupReq.0010-1110</measType>
<measType p="5">SM.PDUSessionSetupSucc.0010-1110</measType>
<measValue measObjLdn="113025289">
<r p="4">4364.0</r>
<r p="5">2739.0</r>
<r p="3">1517.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="113025290">
<r p="4">4742.0</r>
<r p="5">3184.0</r>
<r p="3">1459.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="113025296">
<r p="4">5264.0</r>
<r p="5">3545.0</r>
<r p="3">1629.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="82268687">
<r p="4">6952.0</r>
<r p="5">4337.0</r>
<r p="3">2363</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="82268689">
<r p="4">4229.0</r>
<r p="5">3021.0</r>
<r p="3">1135.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="95697155">
<r p="4">4364.0</r>
<r p="5">3201.0</r>
<r p="3">1054.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="95697174">
<r p="4">7041.0</r>
<r p="5">4229.0</r>
<r p="3">2599.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="95697175">
<r p="1">3502.0</r>
<r p="2">2598.0</r>
<r p="3">851.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="95697176">
<r p="1">4858.0</r>
<r p="2">3430.0</r>
<r p="3">1295.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="103597825">
<r p="1">5134.0</r>
<r p="2">3135.0</r>
<r p="3">1847.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="103597826">
<r p="1">4773.0</r>
<r p="2">3007.0</r>
<r p="3">1650.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="84327425">
<r p="1">4573.0</r>
<r p="2">3347.0</r>
<r p="3">1111.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="84327426">
<r p="1">4316.0</r>
<r p="2">3126.0</r>
<r p="3">1102.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="103593999">
<r p="1">5314.0</r>
<r p="2">3271.0</r>
<r p="3">1860.0</r>
<suspect>false</suspect>
</measValue>
<measValue measObjLdn="103594000">
<r p="1">5037.0</r>
<r p="2">3732.0</r>
<r p="3">1193.0</r>
<suspect>false</suspect>
</measValue>
</measInfo>
</measData>
<fileFooter>
<measCollec endTime="2020-10-14T14:39:20.474"/>
</fileFooter>
</measCollecFile>
Impacts:
RAN Simulator:
PM data generation API should include the capability to store it in mongo DB.
Step2: ML Online Training