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# Copyright 2022-2023 ETSI TeraFlowSDN - TFS OSG (https://tfs.etsi.org/)
#
# 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 logging, pandas, threading
from typing import Dict, Optional, Tuple
from sklearn.cluster import KMeans
from common.proto.context_pb2 import Slice
from common.tools.grpc.Tools import grpc_message_to_json_string
from .Constants import SLICE_GROUPS
from .MetricsExporter import MetricsExporter
from .Tools import (
add_slice_to_group, create_slice_groups, get_slice_grouping_parameters, is_slice_grouping_enabled,
remove_slice_from_group)
LOGGER = logging.getLogger(__name__)
class SliceGrouper:
def __init__(self) -> None:
self._lock = threading.Lock()
self._is_enabled = is_slice_grouping_enabled()
if not self._is_enabled: return
metrics_exporter = MetricsExporter()
metrics_exporter.create_table()
self._slice_groups = create_slice_groups(SLICE_GROUPS)
# Initialize and fit K-Means with the pre-defined clusters we want, i.e., one per slice group
df_groups = pandas.DataFrame(SLICE_GROUPS, columns=['name', 'availability', 'capacity_gbps'])
k_means = KMeans(n_clusters=df_groups.shape[0])
k_means.fit(df_groups[['availability', 'capacity_gbps']])
df_groups['label'] = k_means.predict(df_groups[['availability', 'capacity_gbps']])
self._k_means = k_means
self._df_groups = df_groups
self._group_mapping : Dict[str, Dict] = {
group['name']:{k:v for k,v in group.items() if k != 'name'}
for group in list(df_groups.to_dict('records'))
}
label_to_group = {}
for group_name,group_attrs in self._group_mapping.items():
label = group_attrs['label']
availability = group_attrs['availability']
capacity_gbps = group_attrs['capacity_gbps']
metrics_exporter.export_point(
group_name, group_name, availability, capacity_gbps, is_center=True)
label_to_group[label] = group_name
self._label_to_group = label_to_group
def _select_group(self, slice_obj : Slice) -> Optional[Tuple[str, float, float]]:
with self._lock:
grouping_parameters = get_slice_grouping_parameters(slice_obj)
LOGGER.debug('[_select_group] grouping_parameters={:s}'.format(str(grouping_parameters)))
if grouping_parameters is None: return None
sample = pandas.DataFrame([grouping_parameters], columns=['availability', 'capacity_gbps'])
sample['label'] = self._k_means.predict(sample)
sample = sample.to_dict('records')[0] # pylint: disable=unsubscriptable-object
LOGGER.debug('[_select_group] sample={:s}'.format(str(sample)))
label = sample['label']
availability = sample['availability']
capacity_gbps = sample['capacity_gbps']
group_name = self._label_to_group[label]
LOGGER.debug('[_select_group] group_name={:s}'.format(str(group_name)))
return group_name, availability, capacity_gbps
@property
def is_enabled(self): return self._is_enabled
def group(self, slice_obj : Slice) -> bool:
LOGGER.debug('[group] slice_obj={:s}'.format(grpc_message_to_json_string(slice_obj)))
selected_group = self._select_group(slice_obj)
LOGGER.debug('[group] selected_group={:s}'.format(str(selected_group)))
if selected_group is None: return False
return add_slice_to_group(slice_obj, selected_group)
def ungroup(self, slice_obj : Slice) -> bool:
LOGGER.debug('[ungroup] slice_obj={:s}'.format(grpc_message_to_json_string(slice_obj)))
selected_group = self._select_group(slice_obj)
LOGGER.debug('[ungroup] selected_group={:s}'.format(str(selected_group)))
if selected_group is None: return False
return remove_slice_from_group(slice_obj, selected_group)