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mape-k_ob.py
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from time import sleep
from copy import deepcopy
from analyser import Analyser
from executor import execute
from monitor import Monitor
from knowledge import load_pickle
from keras.models import load_model
ML_ADAPT_PROACTIVE_MODE = 'MPS'
ML_ADAPT_GENERATION_APPROACH = 'homogeneous'
WORKLOAD = 'clarknet'
time = '1m'
offset = ''
METRICS = {
'pods': {
'current': 0,
'desired': 0.8,
'models': {},
'query': 'sum(kube_deployment_spec_replicas{namespace="$n", deployment=~"$s.*"}) by ('
'deployment)',
'time_series': []
},
'cpu': {
'current': 0,
'desired': 0.8,
'max': 10,
'models': {},
'query': 'sum(rate(container_cpu_usage_seconds_total{image!="", namespace="$n", '
'pod=~"$s.*"}[5m]' + offset + ')) / '
'sum(kube_pod_container_resource_requests{namespace="$n", pod=~"$s.*", resource="cpu"}' + offset + ')',
'time_series': []
},
'memory': {
'current': 0,
'models': {},
'query': 'sum(max_over_time(container_memory_working_set_bytes{id=~".*kubepods.*", namespace="$n",'
'pod=~"$s-.*", name!~".*POD.*", container=""}[1m]' + offset + '))',
'time_series': []
},
'rt': {
'current': 0,
'desired': 0.8,
'models': {},
'query': '((histogram_quantile(0.95, sum(irate(istio_request_duration_milliseconds_bucket{reporter="destination", '
'destination_workload_namespace=~"$n", destination_workload=~"$s.*"}[1m]' + offset + ')) by '
'(le, destination_workload))) / 1000)',
'time_series': []
},
'tp': {
'current': 0,
'desired': 0.8,
'models': {},
'query': 'round(sum(irate(istio_requests_total{reporter="destination", destination_workload=~"$s.*"}[1m]' + offset + ')) by ('
'destination_workload), 0.001)',
'time_series': []
}
}
if WORKLOAD == 'alibaba2':
models = ['mlp', 'svr', 'mlp', 'svr', 'mlp', 'mlp']
elif WORKLOAD == 'alibaba3':
models = ['mlp', 'svr', 'mlp', 'svr', 'svr', 'svr']
elif WORKLOAD == 'worldcup98':
models = ['mlp', 'svr', 'mlp', 'svr', 'mlp', 'svr']
elif WORKLOAD == 'nasa':
models = ['mlp', 'mlp', 'svr', 'svr', 'mlp', 'mlp']
elif WORKLOAD == 'alibaba7':
models = ['mlp', 'mlp', 'svr', 'svr', 'mlp', 'mlp']
elif WORKLOAD == 'clarknet':
models = ['mlp', 'mlp', 'mlp', 'mlp', 'svr', 'mlp']
deploys = {
'cartservice': {'queries': deepcopy(METRICS), 'namespace': 'default',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {
'UNIVARIATE': load_pickle('OB/UNIVARIATE/cartservice/' + WORKLOAD + '/cartservice' + models[0] + '20'),
'MPS': [load_pickle(
'OB/MPS/cartservice/' + WORKLOAD + '/cartservice' + models[0] + 'bagging' + str(i) + '20')
for i
in range(0, 99)],
'MULTIVARIATE': load_pickle('OB/MULTIVARIATE/cartservice/' + WORKLOAD + '/cartservice')
}},
'checkoutservice': {'queries': deepcopy(METRICS), 'namespace': 'default',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {'UNIVARIATE': load_pickle(
'OB/UNIVARIATE/checkoutservice/' + WORKLOAD + '/checkoutservice' + models[1] + '20'),
'MPS': [load_pickle(
'OB/MPS/checkoutservice/' + WORKLOAD + '/checkoutservice' + models[1] + 'bagging'
+ str(i) + '20') for i in range(0, 99)],
'MULTIVARIATE': load_pickle(
'OB/MULTIVARIATE/checkoutservice/' + WORKLOAD + '/checkoutservice')
}
},
'currencyservice': {'queries': deepcopy(METRICS), 'namespace': 'default',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {
'UNIVARIATE': load_pickle(
'OB/UNIVARIATE/currencyservice/' + WORKLOAD + '/currencyservice' + models[2] + '20'),
'MPS': [
load_pickle(
'OB/MPS/currencyservice/' + WORKLOAD + '/currencyservice' + models[2] + 'bagging'
+ str(i) + '20') for i in range(0, 99)],
'MULTIVARIATE': load_pickle(
'OB/MULTIVARIATE/currencyservice/' + WORKLOAD + '/currencyservice')
}},
'frontend': {'queries': deepcopy(METRICS), 'namespace': 'default',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {'UNIVARIATE': load_pickle('OB/UNIVARIATE/frontend/' + WORKLOAD + '/frontend' + models[3] + '20'),
'MPS': [
load_pickle('OB/MPS/frontend/' + WORKLOAD + '/frontend' + models[3] + 'bagging'
+ str(i) + '20') for i in range(0, 99)],
'MULTIVARIATE': load_pickle('OB/MULTIVARIATE/frontend/' + WORKLOAD + '/frontend')
}},
'productcatalogservice': {'queries': deepcopy(METRICS), 'namespace': 'default',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {'UNIVARIATE': load_pickle(
'OB/UNIVARIATE/productcatalogservice/' + WORKLOAD + '/productcatalogservice' + models[
4] + '20'),
'MPS': [load_pickle(
'OB/MPS/productcatalogservice/' + WORKLOAD + '/productcatalogservice' + models[
4] + 'bagging'
+ str(i) + '20') for i in range(0, 99)],
'MULTIVARIATE': load_pickle(
'OB/MULTIVARIATE/productcatalogservice/' + WORKLOAD + '/productcatalogservice')
}},
'recommendationservice': {'queries': deepcopy(METRICS), 'namespace': 'default',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {'UNIVARIATE': load_pickle(
'OB/UNIVARIATE/recommendationservice/' + WORKLOAD + '/recommendationservice' + models[
5] + '20'),
'MPS': [load_pickle(
'OB/MPS/recommendationservice/' + WORKLOAD + '/recommendationservice' + models[
5] + 'bagging'
+ str(i) + '20') for i in range(0, 99)],
'MULTIVARIATE': load_pickle(
'OB/MULTIVARIATE/recommendationservice/' + WORKLOAD + '/recommendationservice')
}},
}
#microservices = ['cartservice', 'checkoutservice', 'currencyservice', 'frontend', 'productcatalogservice', 'recommendationservice']
microservices = ['cartservice', 'currencyservice', 'frontend', 'productcatalogservice', 'recommendationservice']
if ML_ADAPT_PROACTIVE_MODE == 'UNIVARIATE':
for microservice in microservices:
del deploys[microservice]['models']['MPS']
del deploys[microservice]['models']['MULTIVARIATE']
del deploys[microservice]['queries']['pods']
del deploys[microservice]['queries']['memory']
del deploys[microservice]['queries']['rt']
del deploys[microservice]['queries']['tp']
elif ML_ADAPT_PROACTIVE_MODE == 'MPS':
for microservice in microservices:
del deploys[microservice]['models']['UNIVARIATE']
del deploys[microservice]['models']['MULTIVARIATE']
del deploys[microservice]['queries']['pods']
del deploys[microservice]['queries']['memory']
del deploys[microservice]['queries']['rt']
del deploys[microservice]['queries']['tp']
elif ML_ADAPT_PROACTIVE_MODE == 'MULTIVARIATE':
for microservice in microservices:
del deploys[microservice]['models']['UNIVARIATE']
del deploys[microservice]['models']['MPS']
deploys[microservice]['models']['MULTIVARIATE']['model'] = load_model(
deploys[microservice]['models']['MULTIVARIATE']['model'])
monitor = Monitor('http://10.66.66.53:30000/prometheus/', end_time='now', start_time='19m', step='60')
analyser = Analyser()
i = 0
while True:
if i < 34:
print('O software está operando reativamente!')
monitor.monitor(deploys, 'reactive')
analyser.reactive(deploys)
else:
print('O software está operando proativamente! ' + ML_ADAPT_PROACTIVE_MODE)
monitor.monitor(deploys, 'proactive')
analyser.proactive(deploys, ML_ADAPT_PROACTIVE_MODE)
execute(deploys)
sleep(30)
i = i + 1