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Usage of all the programs and files have been mentioned below for the reference.
-For AD xapp we require ueMeasReport(UE related dataset)
+For AD xapp we require UEReport (UE related dataset)
AD xApp expect UE data from influxDB database in following structure:
- * There exists database with name "UEData"
- * Inside "UEData" database we have three measurments namely "liveUE", "train", "valid"
+ * There exists database with name "RIC-Test"
+ * Inside "RIC-Test" database we have measurments namely "UEReports"
-Note: *We need to specify influxdb service ruuning in RIC platform in database.py(host = <service name>.<namespace>)
- *InfluxDB will be populated when xApp starts via insert.py. This will be depreciated in next release when there will be data coming from KPIMON
+Note: *We need to update ad_config.ini with influxdb configuration.
+Update host as one of the following:
+ 1. influxdb service ruuning in RIC platform (host = <service name>.<namespace>)
+ OR IP of influxdb pod
+ 2. Update user and password for influxDB instance
+
+To polpulate influxdb with static data provided in .csv (ue.csv).
+ 1. Run "python3 insert.py"
+ 2. Wait for few minutes before deploying AD xApp
+ Note: This will be depreciated in next release when there will be data coming from KPIMON
-Need to update this file each time when there is any modifications in the following components.
-main.py:
-* Initiates xapp api, populated influxDB with data and runs the entry() using xapp.run()
-* If Model is not present in the current path, run train() to train the model for the prediction.
-* Call predict function to perform the following activities for every 10 milisecond.
- a) Currently read the input from "liveUE" measurments and iterate through it. (Needs to update: To iterate every 10 miliseconds and fetch latest sample from influxDB)
- b) Detect anomalous records for the inputs
- c) send the UEID, DU-ID, Degradation type and timestamp for the anomalous records to the Traffic Steering (via rmr with the message type as 30003)
- d) Get the acknowledgement message from the traffic steering
+AD xApp performs following:
+
+* Initiates xapp api, make connection with influxDB and runs the entry() using xapp.run()
+* If Model is not present in the current path,
+ a) Read history data from InfluxDB
+ b) apply pre-processing steps
+ c) trigger Training of ML model.
+ d) after model validation, save transformation, model artifacts
+* Detect anomalous user in real-time.
+ a) Read live data from influxDB every 0.5 second
+ b) Detect anomalous records on given input
+ c) Investigate degradation type for anomalous users
+* send the ue-id, DU-ID, Degradation type and timestamp for the anomalous records to the Traffic Steering (via rmr with the message type as 30003)
+* Get the acknowledgement message from the traffic steering
+* store xApp result in "AD" measurement of influxDB
Note: Need to implement the logic if we do not get the acknowledgment from the TS. (How xapp api handle this?)
-
-ad_train.py - Fetch "train" and "valid"(labelled dataset) measurments from influxDB for build and testing Isolation Forest model. Save final model.
-
- Model: Model has been trained using history data feteched from influxdb for all UE's
- validation: we need to have smaller sample dataset(labelled) for validation in influxDB for model validation.
-
-processing.py:
-It performs the following activities:
-* Columns that are not useful for the prediction will be dropped(UEID, Category, & Timestamp)
-* Filetered numeric data type(as per problem need)
-* verify and drop the highly correlated parameters
-* Use Transformation for makine all parameters in same scale and saved transformer info.
-* returns remaining parameters required for training and testing
-
-
-ad_model.py:
-* Call Predict method to get the anomalous users and send information related to anomalous user to Traffic steering xapp
-
-database.py
-* This module creates connection to influxDB and have methods to read and write data into influxDB
-
-
Code Review / ric-app / ad.git / blobdiff