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#
# -------------------------------------------------------------------------
# Copyright (C) 2020 Wipro Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# -------------------------------------------------------------------------
#
import itertools
import uuid
from oslo_log import log
from conductor.data.plugins.inventory_provider import base
from conductor.data.plugins.inventory_provider.candidates.candidate import Candidate
from conductor.data.plugins.inventory_provider.candidates.slice_profiles_candidate import SliceProfilesCandidate
LOG = log.getLogger(__name__)
class Generator(base.InventoryProviderBase):
def __init__(self):
"""Initialize variables"""
pass
def name(self):
"""Return human-readable name."""
return "generator"
def resolve_demands(self, demands, plan_info, triage_translator_data):
"""Resolve demands into candidate list"""
resolved_demands = {}
for name, requirements in demands.items():
resolved_demands[name] = []
for requirement in requirements:
inventory_type = requirement.get('inventory_type').lower()
candidate_uniqueness = requirement.get('unique', 'true')
filtering_attributes = requirement.get('filtering_attributes')
resolved_demands[name].extend(self.generate_candidates(inventory_type,
filtering_attributes,
candidate_uniqueness))
return resolved_demands
def generate_candidates(self, inventory_type, filtering_attributes, candidate_uniqueness):
if inventory_type == "slice_profiles":
return self.generate_slice_profile_candidates(filtering_attributes, inventory_type, candidate_uniqueness)
else:
LOG.debug("No functionality implemented for \
generating candidates for inventory_type {}".format(inventory_type))
return []
def generate_slice_profile_candidates(self, filtering_attributes, inventory_type, candidate_uniqueness):
"""Generates a list of slice profile candidate based on the filtering attributes,
A sample filtering attribute is given below
filtering_attributes = {'core': {'latency': {'min': 15, 'max': 20, 'steps': 1},
'reliability': {'values': [99.999]}},
'ran': {'latency': {'min': 10, 'max': 20, 'steps': 1},
'reliability': {'values': [99.99]},
'coverage_area_ta_list': {'values': ['City: Chennai']}}}
It will generate slice profile combination from the attributes for each subnet and
generates combination of slice profile tuples from the each subnet.
"""
subnet_combinations = {}
for subnet, attributes in filtering_attributes.items():
attribute_names, attribute_combinations = generate_combinations(attributes)
subnet_combinations[subnet] = organize_combinations(attribute_names, attribute_combinations)
subnet_names, slice_profile_combinations = get_combinations_from_dict(subnet_combinations)
organized_combinations = organize_combinations(subnet_names, slice_profile_combinations)
candidates = []
for combination in organized_combinations:
info = Candidate.build_candidate_info(self.name(), inventory_type, 1.0, candidate_uniqueness,
str(uuid.uuid4()))
candidate = SliceProfilesCandidate(info=info, subnet_requirements=combination)
candidates.append(candidate.convert_nested_dict_to_dict())
return candidates
def generate_combinations(attributes):
"""Generates all combination of the given attribute values.
The params can have a values list or range(min, max)
from which the combinations are generated.
"""
attr = dict()
for attribute, attr_params in attributes.items():
values = attr_params.get('values')
if not values:
values = range(attr_params.get('min', 1), attr_params.get('max'),
attr_params.get('steps', 1))
attr[attribute] = values
return get_combinations_from_dict(attr)
def get_combinations_from_dict(attr):
"""Generates combinations from a dictionary containing lists
Input:
attr = {"latency": [1,2,3],
"reliability": [99.99, 99.9]
}
Output:
attribute_name: ["latency", "reliability"]
attribute_combinations: [[1,99.99], [2,99.99], [3,99.99], [1,99.9], [2,99.9], [3,99.9]]
"""
attribute_names = list(attr.keys())
attribute_combinations = list(itertools.product(*attr.values()))
return attribute_names, attribute_combinations
def organize_combinations(attribute_names, attribute_combinations):
"""Organise the generated combinations into list of dicts.
Input:
attribute_name: ["latency", "reliability"]
attribute_combinations: [[1,99.99], [2,99.99], [3,99.99], [1,99.9], [2,99.9], [3,99.9]]
Output:
combinations = [{'latency': 1, 'reliability': 99.99},
{'latency': 2, 'reliability': 99.99},
{'latency': 3, 'reliability': 99.99},
{'latency': 1, 'reliability': 99.9},
{'latency': 2, 'reliability': 99.9},
{'latency': 3, 'reliability': 99.9}
]
"""
combinations = []
for combination in attribute_combinations:
comb = {}
for (name, value) in zip(attribute_names, combination):
comb[name] = value
combinations.append(comb)
return combinations
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