def clustered_hashrouting_cache_placement(topology,
cache_budget,
n_clusters,
policy,
distance='delay',
**kwargs):
"""Deploy caching nodes for hashrouting in with clusters
Parameters
----------
topology : Topology
The topology object
cache_budget : int
The cumulative cache budget
n_clusters : int
The number of clusters
policy : str (node_const | cluster_const)
The expected global cache hit ratio
distance : str
The attribute used to quantify distance between pairs of nodes.
Default is 'delay'
"""
icr_candidates = topology.graph['icr_candidates']
if n_clusters <= 0 or n_clusters > len(icr_candidates):
raise ValueError("The number of cluster must be positive and <= the "
"number of ICR candidate nodes")
elif n_clusters == 1:
clusters = [set(icr_candidates)]
elif n_clusters == len(icr_candidates):
clusters = [set([v]) for v in icr_candidates]
else:
clusters = compute_clusters(topology,
n_clusters,
distance=distance,
nbunch=icr_candidates,
n_iter=100)
deploy_clusters(topology, clusters, assign_src_rcv=True)
if policy == 'node_const':
# Each node is assigned the same amount of caching space
cache_size = iround(cache_budget / len(icr_candidates))
if cache_size == 0:
return
for v in icr_candidates:
topology.node[v]['stack'][1]['cache_size'] = cache_size
elif policy == 'cluster_const':
cluster_cache_size = iround(cache_budget / n_clusters)
for cluster in topology.graph['clusters']:
cache_size = iround(cluster_cache_size / len(cluster))
for v in cluster:
if v not in icr_candidates:
continue
topology.node[v]['stack'][1]['cache_size'] = cache_size
else:
raise ValueError('clustering policy %s not supported' % policy)
def clustered_hashrouting_cache_placement(topology, cache_budget, n_clusters,
policy, distance='delay', **kwargs):
"""Deploy caching nodes for hashrouting in with clusters
Parameters
----------
topology : Topology
The topology object
cache_budget : int
The cumulative cache budget
n_clusters : int
The number of clusters
policy : str (node_const | cluster_const)
The expected global cache hit ratio
distance : str
The attribute used to quantify distance between pairs of nodes.
Default is 'delay'
"""
icr_candidates = topology.graph['icr_candidates']
if n_clusters <= 0 or n_clusters > len(icr_candidates):
raise ValueError("The number of cluster must be positive and <= the "
"number of ICR candidate nodes")
elif n_clusters == 1:
clusters = [set(icr_candidates)]
elif n_clusters == len(icr_candidates):
clusters = [set([v]) for v in icr_candidates]
else:
clusters = compute_clusters(topology, n_clusters, distance=distance,
nbunch=icr_candidates, n_iter=100)
deploy_clusters(topology, clusters, assign_src_rcv=True)
if policy == 'node_const':
# Each node is assigned the same amount of caching space
cache_size = iround(cache_budget / len(icr_candidates))
if cache_size == 0:
return
for v in icr_candidates:
topology.node[v]['stack'][1]['cache_size'] = cache_size
elif policy == 'cluster_const':
cluster_cache_size = iround(cache_budget / n_clusters)
for cluster in topology.graph['clusters']:
cache_size = iround(cluster_cache_size / len(cluster))
for v in cluster:
if v not in icr_candidates:
continue
topology.node[v]['stack'][1]['cache_size'] = cache_size
else:
raise ValueError('clustering policy %s not supported' % policy)
def clustered_hashrouting_cache_placement(topology,
cache_budget,
n_clusters,
policy,
distance='delay',
**kwargs):
"""Deploy caching nodes for hashrouting in with clusters
Parameters
----------
topology : Topology
The topology object
cache_budget : int
The cumulative cache budget
n_clusters : int
The number of clusters
policy : str (node_const | cluster_const)
The expected global cache hit ratio
distance : str
The attribute used to quantify distance between pairs of nodes.
Default is 'delay'
References
----------
.. [1] L. Saino, I. Psaras and G. Pavlou, Framework and Algorithms for
Operator-managed Content Caching, in IEEE Transactions on
Network and Service Management (TNSM), Volume 17, Issue 1, March 2020
https://doi.org/10.1109/TNSM.2019.2956525
.. [2] L. Saino, On the Design of Efficient Caching Systems, Ph.D. thesis
University College London, Dec. 2015. Available:
http://discovery.ucl.ac.uk/1473436/
"""
icr_candidates = topology.graph['icr_candidates']
if n_clusters <= 0 or n_clusters > len(icr_candidates):
raise ValueError("The number of cluster must be positive and <= the "
"number of ICR candidate nodes")
elif n_clusters == 1:
clusters = [set(icr_candidates)]
elif n_clusters == len(icr_candidates):
clusters = [set([v]) for v in icr_candidates]
else:
clusters = compute_clusters(topology,
n_clusters,
distance=distance,
nbunch=icr_candidates,
n_iter=100)
deploy_clusters(topology, clusters, assign_src_rcv=True)
if policy == 'node_const':
# Each node is assigned the same amount of caching space
cache_size = iround(cache_budget / len(icr_candidates))
if cache_size == 0:
return
for v in icr_candidates:
topology.node[v]['stack'][1]['cache_size'] = cache_size
elif policy == 'cluster_const':
cluster_cache_size = iround(cache_budget / n_clusters)
for cluster in topology.graph['clusters']:
cache_size = iround(cluster_cache_size / len(cluster))
for v in cluster:
if v not in icr_candidates:
continue
topology.node[v]['stack'][1]['cache_size'] = cache_size
else:
raise ValueError('clustering policy %s not supported' % policy)
