X-Git-Url: https://gerrit.o-ran-sc.org/r/gitweb?a=blobdiff_plain;f=README.txt;h=624ded5e0429d950bec05ff2c6777d617dde08d6;hb=16f8a7bd1bcc93d5de0db1ea0bc7e866853413b4;hp=2e5535079b2da8bb1d88211c1b9368b84acdafac;hpb=297dbd6245ec69571c8ad7091a18cbe9c7ba2488;p=ric-app%2Fad.git

diff --git a/README.txt b/README.txt
index 2e55350..624ded5 100644
--- a/README.txt
+++ b/README.txt
@@ -15,31 +15,40 @@
 # ==================================================================================
 
 Usage of all the programs and files have been mentioned below for the reference.
-Need to update this file each time when there is any modifications in the following components. 
+For AD xapp we require UEReport (UE related dataset)
 
-main.py: 
-* Initiates xapp api and runs the entry() using xapp.run()
-* If RF model is not present in the path, run train() to train the model for the prediction.
-  Call predict function for every 1 second(for now as we are using simulated data).
-* Read the input csv file that has both normal and anomalous data.
-* Simulate diff UEIDs that participate in the anomaly by randomly selecting records from this scoring data set
-* Send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003)
-* Get the acknowledgement message from the traffic steering.
+AD xApp expect UE data from influxDB database in following structure:
+	* There exists database with name "RIC-Test"
+	* Inside "RIC-Test" database we have measurments namely "UEReports"
+	
+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
 
-ad_train.py - Read all the csv files in the current path and create trained model(RF)
+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
+	
 
-processing.py:
-It performs the following activities:
-* Columns that are not useful for the prediction will be dropped(UEID, Category, & Timestamp)
-* Convert integer and float type into numeric data type.
-* verify and drop the highly correlated parameters.
-* returns UEID, timestamp and category for the anamolous entries.
+AD xApp performs following:
 
-ad_model.py: 
-* Extract all the unique UEID and filters only the randomly selected UEID(this step will be removed when we implement in sdl way of getting the UEID).
-* Call Predict method to get the final data for the randomly selected UEID.
-
-tb_format.py:
-* start the preprocessing, processing steps using the keycolumns
-* populate current timestamp value for MeasTimestampRF
+* 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
+* Listens to RMR port for A1 policy (message type 20011) in a format given below. Which consists throughput threshold parameter (default: 70%) for an degradataion event to qualify for a handover
+   {'operation': 'CREATE', 'payload': '{\"thp_threshold\":74}', 'policy_instance_id': 'demo-1', 'policy_type_id': '9997'}"}
+* Send the ue-id, DU-ID, Degradation type and timestamp for the qualified 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?)