def test_arbitrary(self):
topologies = [
1221,
1239,
1755,
3257,
3967,
6461,
]
n_contents = 10000
network_cache = 0.1
hit_ratio = 0.2
results = {
1221: 104.43627218934938,
1239: 129.9188933590089,
3257: 102.65934570425449,
1755: 94.2634958382883,
6461: 168.74678558156853,
3967: 132.65163549797435,
}
for asn in topologies:
# Set up topology
topo = scenarios.topology_rocketfuel_latency(asn, source_ratio=0.1)
scenarios.uniform_cache_placement(topo, n_contents * network_cache)
sources = topo.sources()
receivers = topo.receivers()
req_rates = {v: 1 / len(receivers) for v in receivers}
source_content_ratio = {v: 1 / len(sources) for v in sources}
# Run experiment and validate results
latency = cacheperf.hashrouting_model(topo, "SYMM", hit_ratio,
source_content_ratio,
req_rates)
assert round(abs(results[asn] - latency), 7) == 0
def topology_four_child_tree(network_cache=0.05, n_contents=100000, seed=None):
"""
Returns a tree topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
h = 5 # depth of the tree
topology = fnss.k_ary_tree_topology(4, h)
topology.add_node(1365, depth=-1)
topology.add_path([0, 1365])
receivers = [
v for v in topology.nodes_iter() if topology.node[v]['depth'] == h
]
sources = [
v for v in topology.nodes_iter() if topology.node[v]['depth'] == -1
]
caches = [
v for v in topology.nodes_iter()
if topology.node[v]['depth'] >= 0 and topology.node[v]['depth'] < h
]
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology,
range(1, n_contents + 1),
sources,
seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source',
{'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
cache_placement = uniform_cache_placement(topology,
network_cache * n_contents,
caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms',
[(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
return topology
def topology_wide(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on GARR topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
topology = fnss.parse_topology_zoo(
path.join(TOPOLOGY_RESOURCES_DIR, 'WideJpn.graphml')).to_undirected()
# sources are nodes representing neighbouring AS's
sources = [9, 8, 11, 13, 12, 15, 14, 17, 16, 19, 18]
# receivers are internal nodes with degree = 1
receivers = [27, 28, 3, 5, 4, 7]
# caches are all remaining nodes --> 27 caches
caches = [n for n in topology.nodes() if n not in receivers + sources]
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology,
range(1, n_contents + 1),
sources,
seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source',
{'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
# label links as internal or external
for u, v in topology.edges():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms',
[(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
cache_placement = uniform_cache_placement(topology,
network_cache * n_contents,
caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_path(network_cache=0.05, n_contents=100000, n=3, seed=None):
"""
Return a scenario based on path topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
# 240 nodes in the main component
topology = fnss.line_topology(n)
receivers = [0]
caches = range(1, n - 1)
sources = [n - 1]
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology,
range(1, n_contents + 1),
sources,
seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source',
{'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms',
[(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
cache_placement = uniform_cache_placement(topology,
network_cache * n_contents,
caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_garr(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on GARR topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
topology = fnss.parse_topology_zoo(path.join(TOPOLOGY_RESOURCES_DIR, 'Garr201201.graphml')).to_undirected()
# sources are nodes representing neighbouring AS's
sources = [0, 2, 3, 5, 13, 16, 23, 24, 25, 27, 51, 52, 54]
# receivers are internal nodes with degree = 1
receivers = [1, 7, 8, 9, 11, 12, 19, 26, 28, 30, 32, 33, 41, 42, 43, 47, 48, 50, 53, 57, 60]
# caches are all remaining nodes --> 27 caches
caches = [n for n in topology.nodes() if n not in receivers + sources]
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology, range(1, n_contents+1),
sources, seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
# label links as internal or external
for u, v in topology.edges():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms',[(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_four_child_tree(network_cache=0.05, n_contents=100000, seed=None):
"""
Returns a tree topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
h = 5 # depth of the tree
topology = fnss.k_ary_tree_topology(4, h)
topology.add_node(1365, depth=-1)
topology.add_path([0, 1365])
receivers = [v for v in topology.nodes_iter()
if topology.node[v]['depth'] == h]
sources = [v for v in topology.nodes_iter()
if topology.node[v]['depth'] == -1]
caches = [v for v in topology.nodes_iter()
if topology.node[v]['depth'] >= 0
and topology.node[v]['depth'] < h]
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology, range(1, n_contents+1), sources, seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms', [(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
return topology
def topology_path(network_cache=0.05, n_contents=100000, n=3, seed=None):
"""
Return a scenario based on path topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
# 240 nodes in the main component
topology = fnss.line_topology(n)
receivers = [0]
caches = range(1, n-1)
sources = [n-1]
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology, range(1, n_contents+1), sources, seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms', [(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_geant_custom(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on GEANT topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
topology = fnss.parse_topology_zoo(
path.join(TOPOLOGY_RESOURCES_DIR,
"Geant2012.graphml")).to_undirected()
topology = list(nx.connected_component_subgraphs(topology))[0]
deg = nx.degree(topology)
caches = [v for v in topology.nodes()] # 38 nodes
cache_catogory = [
1, 4, 2, 1, 3, 3, 3, 1, 4, 3, 2, 1, 1, 2, 1, 4, 2, 3, 1, 3, 3, 2, 3, 4,
4, 2, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 1, 4, 1
]
count = 0
for node in caches:
fnss.add_stack(topology, node, 'receiver', {})
count += 1
# attach sources to topology
source_attachments = [v for v in topology.nodes()
if deg[v] == 2] # 13 nodes
sources = []
for v in source_attachments:
u = v + 1000 # node ID of source
topology.add_edge(v, u)
sources.append(u)
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology,
range(1, n_contents + 1),
sources,
seed=seed)
# add stacks to nodes
for v in sources:
fnss.add_stack(topology, v, 'source',
{'contents': content_placement[v]})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms',
[(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
# place caches
cache_placement = uniform_cache_placement(topology,
network_cache * n_contents,
caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_tiscali(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on Tiscali topology, parsed from RocketFuel dataset
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
# 240 nodes in the main component
topology = fnss.parse_rocketfuel_isp_map(
path.join(TOPOLOGY_RESOURCES_DIR, '3257.r0.cch')).to_undirected()
topology = list(nx.connected_component_subgraphs(topology))[0]
# degree of nodes
deg = nx.degree(topology)
# nodes with degree = 1
onedeg = [v for v in topology.nodes() if deg[v] == 1] # they are 80
# we select as caches nodes with highest degrees
# we use as min degree 6 --> 36 nodes
# If we changed min degrees, that would be the number of caches we would have:
# Min degree N caches
# 2 160
# 3 102
# 4 75
# 5 50
# 6 36
# 7 30
# 8 26
# 9 19
# 10 16
# 11 12
# 12 11
# 13 7
# 14 3
# 15 3
# 16 2
caches = [v for v in topology.nodes() if deg[v] >= 6] # 36 nodes
# sources are node with degree 1 whose neighbor has degree at least equal to 5
# we assume that sources are nodes connected to a hub
# they are 44
sources = [
v for v in onedeg if deg[list(topology.edge[v].keys())[0]] > 4.5
] # they are
# receivers are node with degree 1 whose neighbor has degree at most equal to 4
# we assume that receivers are nodes not well connected to the network
# they are 36
receivers = [
v for v in onedeg if deg[list(topology.edge[v].keys())[0]] < 4.5
]
# we set router stacks because some strategies will fail if no stacks
# are deployed
routers = [
v for v in topology.nodes() if v not in caches + sources + receivers
]
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology,
range(1, n_contents + 1),
sources,
seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source',
{'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
for v in routers:
fnss.add_stack(topology, v, 'router', {})
# label links as internal or external
for u, v in topology.edges():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms',
[(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
cache_placement = uniform_cache_placement(topology,
network_cache * n_contents,
caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_tiscali(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on Tiscali topology, parsed from RocketFuel dataset
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
# 240 nodes in the main component
topology = fnss.parse_rocketfuel_isp_map(path.join(TOPOLOGY_RESOURCES_DIR,
'3257.r0.cch')
).to_undirected()
topology = nx.connected_component_subgraphs(topology)[0]
# degree of nodes
deg = nx.degree(topology)
# nodes with degree = 1
onedeg = [v for v in topology.nodes() if deg[v] == 1] # they are 80
# we select as caches nodes with highest degrees
# we use as min degree 6 --> 36 nodes
# If we changed min degrees, that would be the number of caches we would have:
# Min degree N caches
# 2 160
# 3 102
# 4 75
# 5 50
# 6 36
# 7 30
# 8 26
# 9 19
# 10 16
# 11 12
# 12 11
# 13 7
# 14 3
# 15 3
# 16 2
caches = [v for v in topology.nodes() if deg[v] >= 6] # 36 nodes
# sources are node with degree 1 whose neighbor has degree at least equal to 5
# we assume that sources are nodes connected to a hub
# they are 44
sources = [v for v in onedeg if deg[list(topology.edge[v].keys())[0]] > 4.5] # they are
# receivers are node with degree 1 whose neighbor has degree at most equal to 4
# we assume that receivers are nodes not well connected to the network
# they are 36
receivers = [v for v in onedeg if deg[list(topology.edge[v].keys())[0]] < 4.5]
# we set router stacks because some strategies will fail if no stacks
# are deployed
routers = [v for v in topology.nodes() if v not in caches + sources + receivers]
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology, range(1, n_contents+1),
sources, seed=seed)
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
for v in routers:
fnss.add_stack(topology, v, 'router', {})
# label links as internal or external
for u, v in topology.edges():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms', [(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_single_cache(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on GEANT topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
# 240 nodes in the main component
#topology = fnss.parse_topology_zoo(path.join(TOPOLOGY_RESOURCES_DIR,
# 'Geant2012.graphml')
# ).to_undirected()
numnodes = 2
topology = fnss.topologies.simplemodels.line_topology(numnodes)
topology = nx.connected_component_subgraphs(topology)[0]
deg = nx.degree(topology)
nodes = topology.nodes()
#receivers = [v for v in topology.nodes() if deg[v] == 1] # 8 nodes
receivers = []
receivers.append(nodes[0])
#caches = [v for v in topology.nodes() if deg[v] > 2] # 19 nodes
caches = []
caches.append(nodes[1])
# attach sources to topology
#source_attachments = [v for v in topology.nodes() if deg[v] == 2] # 13 nodes
source_attachment = caches[0]
#sources = []
#for v in source_attachments:
# u = v + 1000 # node ID of source
# topology.add_edge(v, u)
# sources.append(u)
sources = []
u = source_attachment + 1000
sources.append(u)
topology.add_edge(source_attachment, sources[0])
#routers = [v for v in topology.nodes() if v not in caches + sources + receivers]
#router = nodes[1]
# randomly allocate contents to sources
#content_placement = uniform_content_placement(topology, range(1, n_contents+1), sources, seed=seed)
content_placement = uniform_content_placement(topology, range(1, n_contents+1), sources, seed=seed)
# add stacks to nodes
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
#fnss.add_stack(topology, source, 'source', {'contents': content_placement[source]})
#fnss.add_stack(topology, receiver, 'receiver', {})
#fnss.add_stack(topology, router, 'router', {})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms', [(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
#topology.edge[source_attachment][source]['type'] = 'external'
#fnss.set_weights_constant(topology, 1000.0, [(source_attachment, source)])
#fnss.set_delays_constant(topology, external_link_delay, 'ms', [(source_attachment, source)])
#topology.edge[receiver][cache]['type'] = 'internal'
#topology.edge[router][cache]['type'] = 'internal'
# place caches
#cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_grid(network_cache=0.35, n_contents=100000, seed=None):
# This gives you a 2D grid topology 3x2
topology = fnss.Topology(nx.grid_2d_graph(10,10))
# If you want a 3D grid topology, use this command instead
# This create a 4x3x2 3D grid
# topology = fnss.Topology(nx.grid_graph([4,3,2]))
# TODO: Here place caches, content sources, receivers.
# See other topology generators as examples
topology = nx.connected_component_subgraphs(topology)[0]
deg = nx.degree(topology)
nodes = topology.nodes()
num_sources = 4
num_receivers = 30
source_attachments = []
sources = []
receivers = []
caches = []
chosen_attachments = 0
chosen_receivers = 0
# Random pacement of SOURCES
completed = False
while (completed == False):
x = random.choice(nodes)
if x in source_attachments:
continue
else:
source_attachments.append(x)
chosen_attachments += 1
if chosen_attachments == num_attachments:
completed = True
for v in source_attachments:
u = v + 1000 # node ID of source
topology.add_edge(v, u)
sources.append(u)
# Random placement of RECEIVERS
completed = False
while (completed == False):
x = random.choice(nodes)
if x in source_attachments:
continue
else:
receivers.append(x)
chosen_receivers += 1
if chosen_receivers == num_receivers:
completed = True
# Placement of RECEIVERS
caches = [v for v in nodes if v not in receivers]
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology, range(1, n_contents+1), sources, seed=seed)
# add stacks to nodes
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms', [(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
# place caches
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology
def topology_geant_custom(network_cache=0.05, n_contents=100000, seed=None):
"""
Return a scenario based on GEANT topology
Parameters
----------
network_cache : float
Size of network cache (sum of all caches) normalized by size of content
population
n_contents : int
Size of content population
seed : int, optional
The seed used for random number generation
Returns
-------
topology : fnss.Topology
The topology object
"""
topology = fnss.parse_topology_zoo(path.join(TOPOLOGY_RESOURCES_DIR,
"Geant2012.graphml")
).to_undirected()
topology = list(nx.connected_component_subgraphs(topology))[0]
deg = nx.degree(topology)
caches = [v for v in topology.nodes()] # 38 nodes
cache_catogory = [1,4,2,1,3,3,3,1,4,3,2,1,1,2,1,4,2,3,1,3,3,2,3,4,4,2,3,1,3,1,3,1,3,1,1,3,1,1,4,1]
count = 0
for node in caches:
fnss.add_stack(topology, node, 'receiver', {})
count += 1
# attach sources to topology
source_attachments = [v for v in topology.nodes() if deg[v] == 2] # 13 nodes
sources = []
for v in source_attachments:
u = v + 1000 # node ID of source
topology.add_edge(v, u)
sources.append(u)
# randomly allocate contents to sources
content_placement = uniform_content_placement(topology, range(1, n_contents+1), sources, seed=seed)
# add stacks to nodes
for v in sources:
fnss.add_stack(topology, v, 'source', {'contents': content_placement[v]})
# set weights and delays on all links
fnss.set_weights_constant(topology, 1.0)
fnss.set_delays_constant(topology, INTERNAL_LINK_DELAY, 'ms')
# label links as internal or external
for u, v in topology.edges_iter():
if u in sources or v in sources:
topology.edge[u][v]['type'] = 'external'
# this prevents sources to be used to route traffic
fnss.set_weights_constant(topology, 1000.0, [(u, v)])
fnss.set_delays_constant(topology, EXTERNAL_LINK_DELAY, 'ms', [(u, v)])
else:
topology.edge[u][v]['type'] = 'internal'
# place caches
cache_placement = uniform_cache_placement(topology, network_cache*n_contents, caches)
for node, size in cache_placement.iteritems():
fnss.add_stack(topology, node, 'cache', {'size': size})
return topology