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mape-k_quarkus.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
from time import sleep
ML_ADAPT_PROACTIVE_MODE = 'UNIVARIATE'
WORKLOAD = 'worldcup98'
time = '1m'
offset = ''
METRICS = {
'pods': {
'current': 0,
'desired': 0.8,
'models': {},
'query': 'sum(kube_deployment_status_replicas{namespace="$n", deployment=~"$s.*"}' + offset + ') by ('
'deployment)',
'time_series': []
},
'cpu': {
'current': 0,
'desired': 0.5,
'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': []
},
'heap': {
'current': 0,
'models': {},
'query': '(sum(avg_over_time(base_memory_usedHeap_bytes{pod=~"$s.*"}[1m]' + offset + ')))',
'time_series': []
},
'jvm_total_MarkSweepCompact': {
'current': 0,
'models': {},
'query': '(sum(rate(base_gc_total{name="MarkSweepCompact"}[1m]' + offset + ')))',
'time_series': []
},
'jvm_total_Copy': {
'current': 0,
'models': {},
'query': '(sum(rate(base_gc_total{name="Copy"}[1m]' + offset + ')))',
'time_series': []
},
'jvm_seconds_Copy': {
'current': 0,
'models': {},
'query': '(sum(rate(base_gc_time_total_seconds{name="Copy"}[1m]' + offset + ')) / sum(deriv('
'base_gc_time_total_seconds{name="Copy"}[1m]' + offset + ')))',
'time_series': []
},
'jvm_seconds_MarkSweepCompact': {
'current': 0,
'models': {},
'query': '(sum(rate(base_gc_time_total_seconds{name="MarkSweepCompact"}[1m]' + offset + ')) / sum(deriv('
'base_gc_time_total_seconds{name="MarkSweepCompact"}[1m]' + offset + ')))',
'time_series': []
},
'memory': {
'current': 0,
'models': {},
'query': 'sum(max_over_time(container_memory_working_set_bytes{id=~".*kubepods.*", namespace="$n",'
'pod=~"$s-.*", name!~".*POD.*", container=""}[1m]' + offset + '))',
'time_series': []
},
'rt': {
'current': 0,
'models': {},
'query': 'avg(deriv(base_REST_request_elapsedTime_seconds{pod=~"$s.*",name!~".*POD.*", '
'class="com.acme.crud.FruitResource"}[1m]' + offset + '))',
'time_series': []
},
'tp': {
'current': 0,
'models': {},
'query': 'sum(rate(base_REST_request_total{pod=~"$s.*",name!~".*POD.*",'
'class="com.acme.crud.FruitResource"}[1m]' + offset + '))',
'time_series': []
},
}
if WORKLOAD == 'alibaba3':
models = ['svr']
elif WORKLOAD == 'worldcup98':
models = ['mlp']
elif WORKLOAD == 'nasa':
models = ['mlp']
elif WORKLOAD == 'alibaba7':
models = ['mlp']
elif WORKLOAD == 'clarknet':
models = ['rf']
deploys = {
'quarkus-service': {'queries': deepcopy(METRICS), 'namespace': 'quarkus',
'replicas': {'current': 0, 'needed': 0, 'min': 1, 'max': 10},
'stabilization': {'scale': -1},
'adaptation_command': '',
'models': {
'UNIVARIATE': load_pickle(
'Quarkus/UNIVARIATE/quarkus-service/' + WORKLOAD + '/quarkus-service' + models[0] + '20'),
'MPS': [load_pickle(
'Quarkus/MPS/quarkus-service/' + WORKLOAD + '/quarkus-service' + models[
0] + 'bagging' + str(i) + '20')
for i
in range(0, 99)],
'MULTIVARIATE': load_pickle(
'Quarkus/MULTIVARIATE/quarkus-service/' + WORKLOAD + '/quarkus-service')
}},
}
microservices = ['quarkus-service']
if ML_ADAPT_PROACTIVE_MODE == 'UNIVARIATE':
for microservice in microservices:
del deploys[microservice]['models']['MPS']
del deploys[microservice]['queries']['pods']
del deploys[microservice]['queries']['heap']
del deploys[microservice]['queries']['jvm_total_MarkSweepCompact']
del deploys[microservice]['queries']['jvm_total_Copy']
del deploys[microservice]['queries']['jvm_seconds_Copy']
del deploys[microservice]['queries']['jvm_seconds_MarkSweepCompact']
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]['queries']['pods']
del deploys[microservice]['queries']['heap']
del deploys[microservice]['queries']['jvm_total_MarkSweepCompact']
del deploys[microservice]['queries']['jvm_total_Copy']
del deploys[microservice]['queries']['jvm_seconds_Copy']
del deploys[microservice]['queries']['jvm_seconds_MarkSweepCompact']
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')
# monitor = Monitor('http://172.20.16.232/prometheus/', end_time='now', start_time='19m', step='60')
analyser = Analyser()
sleep(180)
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! ' + WORKLOAD + ' ' + ML_ADAPT_PROACTIVE_MODE)
monitor.monitor(deploys, 'proactive')
analyser.proactive(deploys, ML_ADAPT_PROACTIVE_MODE)
execute(deploys)
sleep(30)
i = i + 1