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 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]
