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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2017 Cisco and/or its affiliates.
#
# 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
import networkx as nx
import os
from netmodel.util.misc import pairwise
from vicn.core.attribute import Attribute, Reference
from vicn.resource.channel import Channel
log = logging.getLogger(__name__)
#------------------------------------------------------------------------------
# Routing strategies
#------------------------------------------------------------------------------
def routing_strategy_spt(G, origins, weight_key = None):
"""Routing strategy : Shortest path tree
This routing strategy uses the Dijkstra algorithm on an undirected graph
to build the shortest path tree towards all origin prefixes.
NOTE: weights are currently unsupported by this strategy.
Args:
G (nx.Graph): network graph
origins (dict): dictionary mapping nodes to the set of prefixes they
are origins for
weight_key (str): key corresponding to weight key in edge data. None
assumes all weights have unit cost
Returns:
generator : returning triplets (source, prefix, next hop)
"""
assert weight_key is None
origin_nodes = origins.keys()
seen = set()
for dst_node in origin_nodes:
if not G.has_node(dst_node):
continue
sssp = nx.shortest_path(G, target = dst_node)
# Notes from the documentation:
# - If only the target is specified, return a dictionary keyed by
# sources with a list of nodes in a shortest path from one of the
# sources to the target.
# - All returned paths include both the source and target in the
# path.
for _, path in sssp.items():
if len(path) == 1:
# Local prefix
continue
for s, d in pairwise(path):
for prefix in origins[dst_node]:
t = (s, prefix, d)
if t in seen:
continue
seen.add(t)
yield t
def routing_strategy_max_flow(G, origins, weight_key = 'capacity'):
"""Routing strategy : Maximum Flow
TODO
Args:
G (nx.Graph): network graph
origins (dict): dictionary mapping nodes to the set of prefixes they
are origins for
weight_key (str): key corresponding to weight key in edge data. None
assumes all weights have unit cost
Returns:
generator : returning triplets (source, prefix, next hop)
"""
assert weight_key is None
origin_nodes = origins.keys()
for dst_node in origin_nodes:
if not G.has_node(dst_node):
continue
for src_node in G.nodes:
if src_node == dst_node:
continue
if not G.has_node(src_node):
continue
_, flow_dict = nx.maximum_flow(G, src_node, dst_node,
capacity=weight_key)
# Notes from the documentation:
# https://networkx.github.io/documentation/networkx-1.10/reference/
# generated/networkx.algorithms.flow.maximum_flow.html
# - flow_dict (dict) – A dictionary containing the value of the
# flow that went through each edge.
for s, d_map in flow_dict.items():
for d, flow in d_map.items():
if flow == 0:
continue
for prefix in origins[dst_node]:
yield s, prefix, d
MAP_ROUTING_STRATEGY = {
'spt' : routing_strategy_spt,
'max_flow' : routing_strategy_max_flow,
}
#------------------------------------------------------------------------------
# L2 and L4/ICN graphs
#------------------------------------------------------------------------------
def _get_l2_graph(groups):
"""
We iterate on all the channels that belong to the same groups as the
resources.
NOTE: We have to make sure the nodes also belong to the group.
"""
G = nx.Graph()
for group in groups:
for channel in group.iter_by_type_str('channel'):
if channel.has_type('emulatedchannel'):
src = channel._ap_if
# XXX bug in reverse collections, resources and not UUIDs seem to be stored inside
if group.name not in [x.name for x in src.node.groups]:
continue
for dst in channel._sta_ifs.values():
if group.name not in [x.name for x in dst.node.groups]:
continue
if G.has_edge(src.node._state.uuid, dst.node._state.uuid):
continue
map_node_interface = { src.node._state.uuid : src._state.uuid,
dst.node._state.uuid: dst._state.uuid}
G.add_edge(src.node._state.uuid, dst.node._state.uuid,
map_node_interface = map_node_interface)
else:
# This is for a normal Channel
for src_it in range(0, len(channel.interfaces)):
src = channel.interfaces[src_it]
if group.name not in [x.name for x in src.node.groups]:
continue
# Iterate over the remaining interface to create all the
# possible combination
for dst_it in range(src_it+1,len(channel.interfaces)):
dst = channel.interfaces[dst_it]
if group.name not in [x.name for x in dst.node.groups]:
continue
if G.has_edge(src.node._state.uuid, dst.node._state.uuid):
continue
map_node_interface = {
src.node._state.uuid : src._state.uuid,
dst.node._state.uuid: dst._state.uuid}
G.add_edge(src.node._state.uuid, dst.node._state.uuid,
map_node_interface = map_node_interface)
return G
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