def get_metrics(graph, metrics='all'):
"""
Returns all the metrics associated with the input graph
:param graph: (NetworkX Graph) Graph to be annotated with data
:param metrics: metric type to be considered. default = all
:return: the list of metrics associated with the graph
"""
metric_list = []
for node in graph.nodes(data=True):
node_name = InfoGraphNode.get_name(node)
node_layer = InfoGraphNode.get_layer(node)
node_type = InfoGraphNode.get_type(node)
# This method supports export of either normal metrics coming
# from telemetry agent or utilization type of metrics.
if metrics == 'all':
node_telemetry_data = InfoGraphNode.get_telemetry_data(node)
else:
node_telemetry_data = InfoGraphNode.get_utilization(node)
metric_list.extend([
"{}@{}@{}@{}".format(node_name, node_layer, node_type,
metric_name).replace(".", "_")
for metric_name in node_telemetry_data.columns.values
if metric_name != 'timestamp'
])
return metric_list
def _create_pandas_data_frame_from_graph(graph, metrics='all'):
"""
Save on csv files the data in the graph.
Stores one csv per node of the graph
:param graph: (NetworkX Graph) Graph to be annotated with data
:param directory: (str) directory where to store csv files
:return: NetworkX Graph annotated with telemetry data
"""
result = pandas.DataFrame()
for node in graph.nodes(data=True):
node_name = InfoGraphNode.get_name(node)
node_layer = InfoGraphNode.get_layer(node)
node_type = InfoGraphNode.get_type(node)
# This method supports export of either normal metrics coming
# from telemetry agent or utilization type of metrics.
if metrics == 'all':
node_telemetry_data = InfoGraphNode.get_telemetry_data(node)
else:
node_telemetry_data = InfoGraphNode.get_utilization(node)
# df = node_telemetry_data.copy()
# LOG.info("Node Name: {} -- Telemetry: {}".format(
# InfoGraphNode.get_name(node),
# InfoGraphNode.get_telemetry_data(node).columns.values
# ))
node_telemetry_data['timestamp'] = node_telemetry_data[
'timestamp'].astype(float)
node_telemetry_data['timestamp'] = node_telemetry_data[
'timestamp'].round()
node_telemetry_data['timestamp'] = node_telemetry_data[
'timestamp'].astype(int)
for metric_name in node_telemetry_data.columns.values:
if metric_name == 'timestamp':
continue
col_name = "{}@{}@{}@{}".\
format(node_name, node_layer, node_type, metric_name)
col_name = col_name.replace(".", "_")
node_telemetry_data = node_telemetry_data.rename(
columns={metric_name: col_name})
# LOG.info("TELEMETRIA: {}".format(node_telemetry_data.columns.values))
if node_telemetry_data.empty or len(
node_telemetry_data.columns) <= 1:
continue
if result.empty:
result = node_telemetry_data.copy()
else:
node_telemetry_data = \
node_telemetry_data.drop_duplicates(subset='timestamp')
result = pandas.merge(result,
node_telemetry_data,
how='outer',
on='timestamp')
# TODO: Try with this removed
# result.set_index(['timestamp'])
return result
def get_correlation(node_a, node_b, metric_a, metric_b):
# TODO: Add node validation
# InfoGraphNode.validateNode(node_a)
# InfoGraphNode.validateNode(node_b)
node_name_a = InfoGraphNode.get_name(node_a)
node_name_b = InfoGraphNode.get_name(node_b)
if metric_a == 'utilization':
telemetry_a = InfoGraphNode.get_utilization(node_a)
else:
telemetry_a = InfoGraphNode.get_telemetry_data(node_a)
if metric_b == 'utilization':
telemetry_b = InfoGraphNode.get_utilization(node_b)
else:
telemetry_b = InfoGraphNode.get_telemetry_data(node_b)
if metric_a not in telemetry_a.columns.values:
raise ValueError(
"Metric {} is not in Telemetry data of Node {}".format(
metric_a, node_name_a))
if metric_b not in telemetry_b.columns.values:
raise ValueError(
"Metric {} is not in Telemetry data of Node {}".format(
metric_b, node_name_b))
if telemetry_a.empty and telemetry_b.empty:
return 0
res = telemetry_a.corrwith(telemetry_b)
df_a = telemetry_a.\
rename(columns={metric_a: "a-{}".format(metric_a)}).astype(float)
df_b = telemetry_b.\
rename(columns={metric_b: "b-{}".format(metric_b)}).astype(float)
correlation = pandas.merge(df_a, df_b, how='outer', on='timestamp')
correlation = correlation.dropna()
res = correlation["a-{}".format(metric_a)].\
corr(correlation["b-{}".format(metric_b)])
return res
def machine_capacity_usage(annotated_subgraph):
"""
This is a type of fingerprint from the infrastructure perspective
"""
# TODO: Validate graph
categories = list()
categories.append(InfoGraphNodeCategory.COMPUTE)
categories.append(InfoGraphNodeCategory.NETWORK)
# TODO: Add a Volume to the workloads to get HD usage
categories.append(InfoGraphNodeCategory.STORAGE)
# TODO: Get telemetry for Memory
categories.append(InfoGraphNodeCategory.MEMORY)
fingerprint = dict()
counter = dict()
for category in categories:
fingerprint[category] = 0
counter[category] = 0
# calculation of the fingerprint on top of the virtual resources
local_subgraph = annotated_subgraph.copy()
local_subgraph.filter_nodes('layer', "virtual")
local_subgraph.filter_nodes('layer', "service")
local_subgraph.filter_nodes('type', 'machine')
for node in local_subgraph.nodes(data=True):
# if Fingerprint._node_is_nic_on_management_net(
# node, annotated_subgraph, mng_net_name):
# continue
name = InfoGraphNode.get_name(node)
category = InfoGraphNode.get_category(node)
utilization = InfoGraphNode.get_utilization(node)
if 'utilization' in utilization.columns.values:
# LOG.info("NODE: {} - CATEGORY: {}".format(name, category))
mean = utilization['utilization'].mean()
fingerprint[category] += mean
counter[category] += 1
# This is just an average
# TODO: Improve the average
for category in categories:
if counter[category] > 0:
fingerprint[category] = \
fingerprint[category] / counter[category]
return fingerprint
def utilization_scores(graph):
"""
Returns a dictionary with the scores of
all the nodes of the graph.
:param graph: InfoGraph
:return: dict[node_name] = score
"""
res = dict()
for node in graph.nodes(data=True):
node_name = InfoGraphNode.get_name(node)
res[node_name] = dict()
util = InfoGraphNode.get_utilization(node)
import analytics_engine.common as common
LOG = common.LOG
res[node_name]['compute'] = 0
res[node_name]['disk'] = 0
res[node_name]['network'] = 0
res[node_name]['memory'] = 0
if (isinstance(util, pandas.DataFrame) and
util.empty) or \
(not isinstance(util, pandas.DataFrame) and
util==None):
continue
# intel/use/
if 'intel/use/compute/utilization' in util:
res[node_name]['compute'] = (
util.get('intel/use/compute/utilization').mean()) / 100.0
elif 'intel/procfs/cpu/utilization_percentage' in util:
res[node_name]['compute'] = (util.get(
'intel/procfs/cpu/utilization_percentage').mean()) / 100.0
if 'intel/use/memory/utilization' in util:
res[node_name]['memory'] = (
util.get('intel/use/memory/utilization').mean()) / 100.0
elif 'intel/procfs/memory/utilization_percentage' in util:
res[node_name]['memory'] = (
util.get('intel/procfs/memory/utilization_percentage'
).mean()) / 100.0
if 'intel/use/disk/utilization' in util:
res[node_name]['disk'] = (
util.get('intel/use/disk/utilization').mean()) / 100.0
elif 'intel/procfs/disk/utilization_percentage' in util:
res[node_name]['disk'] = (util.get(
'intel/procfs/disk/utilization_percentage').mean()) / 100.0
if 'intel/use/network/utilization' in util:
res[node_name]['network'] = (
util.get('intel/use/network/utilization').mean()) / 100.0
elif 'intel/psutil/net/utilization_percentage' in util:
res[node_name]['network'] = (util.get(
'intel/psutil/net/utilization_percentage').mean()) / 100.0
# special handling of cpu, disk & network utilization if node is a machine
if InfoGraphNode.node_is_machine(node):
# mean from all cpu columns
cpu_util = InfoGraphNode.get_compute_utilization(node)
cpu_util['total'] = [
sum(row) / len(row) for index, row in cpu_util.iterrows()
]
res[node_name]['compute'] = cpu_util['total'].mean() / 100
# mean from all disk columns
disk_util = InfoGraphNode.get_disk_utilization(node)
if disk_util.empty:
res[node_name]['disk'] = 0.0
else:
disk_util['total'] = [
sum(row) / len(row)
for index, row in disk_util.iterrows()
]
res[node_name]['disk'] = disk_util['total'].mean() / 100
# mean from all nic columns
net_util = InfoGraphNode.get_network_utilization(node)
if net_util.empty:
res[node_name]['network'] = 0.0
else:
net_util['total'] = [
sum(row) / len(row)
for index, row in net_util.iterrows()
]
res[node_name]['network'] = net_util['total'].mean() / 100
# custom metric
if InfoGraphNode.get_type(
node) == InfoGraphNodeType.DOCKER_CONTAINER:
node_name = InfoGraphNode.get_docker_id(node)
res[node_name] = {}
if 'intel/docker/stats/cgroups/cpu_stats/cpu_usage/percentage' in util.columns:
res[node_name]['compute'] = util[
'intel/docker/stats/cgroups/cpu_stats/cpu_usage/percentage'].mean(
) / 100
else:
res[node_name]['compute'] = 0
if 'intel/docker/stats/cgroups/memory_stats/usage/percentage' in util.columns:
res[node_name]['memory'] = util[
'intel/docker/stats/cgroups/memory_stats/usage/percentage'].mean(
) / 100
else:
res[node_name]['memory'] = 0
if 'intel/docker/stats/network/utilization_percentage' in util.columns:
res[node_name]['network'] = util[
'intel/docker/stats/network/utilization_percentage'].mean(
) / 100
else:
res[node_name]['network'] = 0
if 'intel/docker/stats/cgroups/blkio_stats/io_time_recursive/percentage' in util.columns:
res[node_name]['disk'] = util[
'intel/docker/stats/cgroups/blkio_stats/io_time_recursive/percentage'].mean(
) / 100
else:
res[node_name]['disk'] = 0
return res
def compute_node_resources(annotated_subgraph, hostname=None):
"""
This is a type of fingerprint from the infrastructure perspective
"""
# TODO: Validate graph
data = dict()
statistics = dict()
# Calculation of the fingerprint on top of the virtual resources
local_subgraph = annotated_subgraph.copy()
for node in local_subgraph.nodes(data=True):
layer = InfoGraphNode.get_layer(node)
if layer == InfoGraphNodeLayer.VIRTUAL:
continue
if layer == InfoGraphNodeLayer.SERVICE:
continue
type = InfoGraphNode.get_type(node)
if type == 'core':
continue
# If hostname has been specified, need to take into account only
# nodes that are related to the specific host
attrs = InfoGraphNode.get_attributes(node)
allocation = attrs['allocation'] if 'allocation' in attrs \
else None
if hostname and not hostname == allocation:
continue
name = InfoGraphNode.get_name(node)
statistics[name] = {
'mean': 0,
'median': 0,
'min': 0,
'max': 0,
'var': 0,
'std_dev': 0
}
utilization = InfoGraphNode.get_utilization(node)
try:
utilization = utilization.drop('timestamp', 1)
except ValueError:
utilization = InfoGraphNode.get_utilization(node)
data[name] = utilization
if not data[name].empty:
mean = data[name]['utilization'].mean()
median = (data[name]['utilization']).median()
min = data[name]['utilization'].min()
maximum = data[name]['utilization'].max()
var = data[name]['utilization'].var()
std_dev = math.sqrt(var)
else:
mean = 0
median = 0
min = 0
maximum = 0
var = 0
std_dev = 0
statistics[name] = \
{'mean': mean,
'median': median,
'min': min,
'max': maximum,
'var': var,
'std_dev': std_dev}
return [data, statistics]
def compute_node(annotated_subgraph, hostname=None):
"""
This is a type of fingerprint from the infrastructure perspective
"""
# TODO: Validate graph
data = dict()
statistics = dict()
compute = InfoGraphNodeCategory.COMPUTE
data[compute] = pandas.DataFrame()
statistics[compute] = {
'mean': 0,
'median': 0,
'min': 0,
'max': 0,
'var': 0,
'std_dev': 0
}
network = InfoGraphNodeCategory.NETWORK
data[network] = pandas.DataFrame()
statistics[network] = {
'mean': 0,
'median': 0,
'min': 0,
'max': 0,
'var': 0,
'std_dev': 0
}
storage = InfoGraphNodeCategory.STORAGE
data[storage] = pandas.DataFrame()
statistics[storage] = {
'mean': 0,
'median': 0,
'min': 0,
'max': 0,
'var': 0,
'std_dev': 0
}
memory = InfoGraphNodeCategory.MEMORY
data[memory] = pandas.DataFrame()
statistics[memory] = {
'mean': 0,
'median': 0,
'min': 0,
'max': 0,
'var': 0,
'std_dev': 0
}
# Calculation of the fingerprint on top of the virtual resources
local_subgraph = annotated_subgraph.copy()
for node in local_subgraph.nodes(data=True):
layer = InfoGraphNode.get_layer(node)
is_machine = InfoGraphNode.node_is_machine(node)
if is_machine:
continue
if layer == InfoGraphNodeLayer.VIRTUAL:
continue
if layer == InfoGraphNodeLayer.SERVICE:
continue
# If hostname has been specified, need to take into account only
# nodes that are related to the specific host
attrs = InfoGraphNode.get_attributes(node)
allocation = attrs['allocation'] if 'allocation' in attrs \
else None
if hostname and not hostname == allocation:
continue
category = InfoGraphNode.get_category(node)
utilization = InfoGraphNode.get_utilization(node)
try:
utilization = utilization.drop('timestamp', 1)
except ValueError:
utilization = InfoGraphNode.get_utilization(node)
data[category] = pandas.concat([data[category], utilization])
for category in statistics:
if not data[category].empty:
mean = data[category]['utilization'].mean()
median = (data[category]['utilization']).median()
min = data[category]['utilization'].min()
maximum = data[category]['utilization'].max()
var = data[category]['utilization'].var()
std_dev = math.sqrt(var)
else:
mean = 0
median = 0
min = 0
maximum = 0
var = 0
std_dev = 0
statistics[category] = \
{'mean': mean,
'median': median,
'min': min,
'max': maximum,
'var': var,
'std_dev': std_dev}
return [data, statistics]