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When the performance of the AI model gets worse, it may cause a serious decline in quality of service.
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If a model predicts something, we can calculate the accuracy of the inference with real data after targeting time that model predicted.
If a model decide values or action of other functions, we can measure the credibility of the models' decision by tracking KPIs collected from the environment.
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<proposed monitoring system and scenario>
(#image1)
Monitoring system consists of a monitoring server deployed in the central cluster, monitoring agents distributed in the RICs, pluggable performance analysis modules and additional functions in the dashboard.
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The monitoring agent forwards the stored data to the monitoring server.
Not If all raw data is not necessary to be transferred. Basically, some basically statistical values such as mean,std,min,max and skew are calculated and sent every 5ms. ( time interval and format is flexible )
Since InfluxDB is default time-series DB in RIC,
we can use its default operations for time-series data. (https://docs.influxdata.com/flux/v0/stdlib/universe/)
The monitoring server stores the statistics data in the InfluxDB in the central cluster.
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