Commit 6dfdba09 authored by Guillermo Gomez's avatar Guillermo Gomez
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initial commit with latest version found in https://github.com/Luis-GA/OSM-AI-Agent

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*.pyc
\ No newline at end of file
FROM python:slim
WORKDIR /AI-Agent
COPY requirements.txt ./
RUN pip install -r requirements.txt
ADD prom_lib ./prom_lib
COPY main.py ./
RUN mkdir -p /etc/volume
CMD [ "python", "main.py" ]
\ No newline at end of file
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# OSM-AI-Agent
AI-Agent container for OSM helm chart
from base64 import b64decode
import json
import logging
import os
import requests
from pymongo import MongoClient, DESCENDING
from prom_lib.prometheus_client import PrometheusClient
import datetime
from importlib import import_module
import uuid
logger = logging.getLogger("AI-Agent v8")
stream_handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s %(name)s %(levelname)s: %(message)s')
stream_formatter = logging.Formatter('%(asctime)s %(name)s %(levelname)s: %(message)s')
stream_handler.setFormatter(stream_formatter)
logger.setLevel(logging.DEBUG)
logger.addHandler(stream_handler)
def get_prometheus_data(ns_id, query, step=120, days=2):
"""
Prometheus data requester
:param ns_id: network service instance id
:param query: prometheus query
:param step: time between probe and probe (in seconds)
:param days: interval of data (current datetime - days, current datetime)
:return: Prometheus data: List of tuples [(value of metric, timestamp), ...]
"""
client = PrometheusClient('http://prometheus:9090')
ts_data = client.range_query(query, ns_id, step=step, days=days)
timeseries = ts_data['result'][0]['values']
return timeseries
def get_ns_info():
"""
Network service data requester using MongoDB as datasource
:return: Network Service Data with the following structure:
{
'member-vnf-index-ref': <string>,
'nsi_id': <string>,
'vdu-data': <list>,
'ns_name': <string>,
'vnfs': <list>,
'project_id': <string>,
'scaling-group-descriptor': <string>
}
"""
client = MongoClient('mongo', 27017)
osm = client['osm']
values = {}
vnf = os.environ.get('vnf-id')
vnf_data = osm['vnfrs'].find_one({'_id': vnf})
values['member-vnf-index-ref'] = vnf_data['member-vnf-index-ref']
values['nsi_id'] = vnf_data['nsr-id-ref']
vduData = []
for vdu in vnf_data.get('vdur'):
vduData.append(
{'vdu-id-ref': vdu.get('vdu-id-ref'), 'ip-address': vdu.get('ip-address'), 'name': vdu.get('name')})
values['vdu-data'] = vduData
ns_data = list(osm['nsrs'].find({'_id': values['nsi_id']}))[0]
values['ns_name'] = ns_data.get('name')
values['vnfs'] = ns_data.get('constituent-vnfr-ref', [])
values['project_id'] = ns_data.get('_admin').get('projects_write', [None])[0]
scaling = osm['vnfds'].find_one({'_id': vnf_data['vnfd-id']}).get('scaling-group-descriptor', [{}])[0].get('name')
values['scaling-group-descriptor'] = scaling
return values
def scale_ns(nsi_id, project_id, scaling_group, vnf_index, scale="SCALE_OUT"):
"""
Scale in/out trigger for NS.
:param nsi_id: Network Service instance identification
:param project_id: OSM Project identification
:param scaling_group: Scaling group action (Specified in the VNF descriptor)
:param vnf_index: Virtual Network Function index (Specified in the VNF descriptor)
:param scale: Scale action 2 possibilities: 'SCALE_OUT' Or 'SCALE_IN'
:return: Scaling action response
"""
token = update_token(project_id)
headers = {'Authorization': token, 'accept': 'application/json'}
url = 'https://nbi:9999/osm/nslcm/v1/ns_instances/{}/scale'.format(nsi_id)
scale_data = {
"scaleType": "SCALE_VNF",
"timeout_ns_scale": 1,
"scaleVnfData": {
"scaleVnfType": scale,
"scaleByStepData": {
"scaling-group-descriptor": scaling_group,
"scaling-policy": "string",
"member-vnf-index": vnf_index
}
}
}
response = requests.post(url, data=str(scale_data), verify=False, headers=headers)
delete_token(token)
return response.text
def update_token(project_id):
"""
NBI Token generator to provide authorization access
:param project_id: OSM Project identification
:return: token to perform requests in an authorized way
"""
client = MongoClient('mongo', 27017)['osm']['tokens']
token = os.environ.get('NBI-Token', uuid.uuid4())
date = datetime.datetime.utcnow().timestamp()
token_data = client.find_one({'project_id': project_id}, sort=[('expires', DESCENDING)])
token_data['_id'] = token
token_data['id'] = token
token_data['issued_at'] = date
token_data['expires'] = date + 5
try:
client.delete_one({'id': token})
except Exception as e:
logger.debug(e)
client.insert_one(token_data)
token = "Bearer " + token
return token
def delete_token(token):
"""
Remove used token for scaling actions to be deleted
:param token: token used
:return: None
"""
token = token.split('Bearer ')[-1]
client = MongoClient('mongo', 27017)['osm']['tokens']
client.delete_one({'id': token})
def url_composer(url, port=None):
"""
URL composer
:param url: URL to compose
:param port: optional port to compose
:return: composed URL
"""
if url[0] != 'h':
url = 'http://{}'.format(url)
if port:
url = url + ':' + str(port)
return url
def get_metrics(prediction, values):
"""
Metrics requester in evaluation phase
:param prediction: prediction object defined in the helm chart values
:param values: values obtained by the function get_ns_info
:return: metrics
"""
data = []
url = prediction['monitoring'].get('url')
prom_query = prediction['monitoring'].get('prometheusQuery')
if url:
url = prediction['monitoring']['url']
if url == 'vnf':
url = values['vdu-data']['ip-address']
port = prediction['monitoring']['port']
url = url_composer(url, port)
data = requests.get(url).text
elif prom_query:
stepsTime = prediction['monitoring'].get('stepsTime') * 60
dataWindowTime = prediction['monitoring'].get('dataWindowTime') / 1440
data = get_prometheus_data(values['nsi_id'], prom_query, step=stepsTime, days=dataWindowTime)
logger.info('Metrics requested')
return data
def ai_evaluation(prediction, ai_url, data):
"""
AI evaluation asking the AI Server and being evaluated by the threshold defined in the helm chart
:param prediction: prediction object defined in the helm chart values
:param ai_url: AI URL defined in the helm chart
:param data: metrics data
:return: True for scaling, False for not scaling
"""
url = ai_url.format(prediction['model'])
forecast_data = requests.post(url, data=data).json()
logger.info('prediction requested: {}'.format(forecast_data))
threshold = prediction['threshold']
with open('aux_functions.py', 'w') as out_file:
out_file.write(threshold['logic'])
evaluation_function = getattr(import_module('aux_functions'), threshold['function_name'])
logger.info("importing evaluation function")
return evaluation_function(forecast_data)
def evaluate_v1(config, values):
"""
Evaluation function V1
:param config: configuration object defined in the helm chart values
:param values: values obtained by the function get_ns_info
:return: None
"""
if config['AIServer']['type'] == 'tensorflow':
ai_url = os.path.join(config['AIServer']['url'], config['AIServer']['version'], 'models') + '/{}:predict'
else:
ai_url = config['AIServer']['url']
ai_url = url_composer(ai_url)
logger.info("AI URL : {}".format(ai_url))
for prediction in config['predictions']:
if prediction['active']:
logger.info('Prediction to perform: {}'.format(prediction))
data = get_metrics(prediction, values)
evaluation = ai_evaluation(prediction, ai_url, data)
if evaluation and (len(values['vdu-data']) == 1):
logger.info("SCALING OUT")
scale_ns(values['nsi_id'], values['project_id'], values['scaling-group-descriptor'],
values['member-vnf-index-ref'])
elif not evaluation and len(values['vdu-data']) > 1:
logger.info("SCALING IN")
scale_ns(values['nsi_id'], values['project_id'], values['scaling-group-descriptor'],
values['member-vnf-index-ref'], scale="SCALE_IN")
if __name__ == '__main__':
logger.info('AI Agent V1.0')
config = os.environ.get('config')
if config:
config = b64decode(config)
config = json.loads(config)
logger.info('Config:\n{}'.format(config))
else:
logger.info('No config available')
values = get_ns_info()
logger.info('Values:\n{}'.format(values))
evaluate_v1(config, values)
import requests
from datetime import datetime, timedelta
import time
class PrometheusClient:
apibase = "/api/v1/"
def __init__(self, prometheus_url="http://localhost:9090"):
self.apibase = "/api/v1/"
self.prometheus_url = prometheus_url if prometheus_url[-1:] != '/' else prometheus_url[:-1]
def range_query(self, metric, instance=None, start=None, end=None, step=60, params=None, days=1):
"""
Returns a PrometheusData object loaded with results from a query_range call
:param instance: instance to query
:param days: Number of days to query
:param metric: string of the metric query
:param start: an epoch time for the earliest data point in the range -- default is 24 hours ago
:param end: an epoch time for the latest data point in the range -- default is the current time
:param step: the step size
:param params: additional params to send to the API
:return: PrometheusData
"""
if params is None:
params = {}
params['query'] = metric
params['instance'] = instance
params['step'] = step
if start is None:
start = self._time_to_epoch((datetime.now() - timedelta(days=days)))
if end is None:
end = self._time_to_epoch(datetime.now())
params['start'] = start
params['end'] = end
response = self._fetch('query_range', params)
data = self._extract_data(response)
return data
@staticmethod
def _time_to_epoch(t):
"""
A simple utility to turn a datetime object into a timestamp
:param t: datetime object
:return: integer
"""
return int(time.mktime(t.timetuple()))
def _fetch(self, resource, params=None):
if params is None:
params = {}
full_url = self.prometheus_url + self.apibase + resource
res = requests.get(full_url, params)
return res.json()
@staticmethod
def _extract_data(jsondata):
if jsondata['status'] != 'success':
data = []
else:
data = jsondata['data']
return data
@staticmethod
def _handle_results(data):
results = data
return results
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