def do_metrics(options, topo, g):
'''Compute the metrics for a single topology.'''
print("==========options")
print(options)
print("computing metricss for topo: %s" % topo)
controllers = get_controllers(g, options)
filename = get_filename(topo, options, controllers)
data = {} # See top for data schema details.
apsp = nx.all_pairs_dijkstra_path_length(g)
apsp_paths = nx.all_pairs_dijkstra_path(g)
extra_params = get_extra_params(g)
if options.use_prior:
data = read_json_file(filename)
else:
start = time.time()
weighted = True
metrics.run_all_combos(options.metrics, g, controllers, data, apsp,
apsp_paths, weighted, options.write_dist,
options.write_combos, extra_params,
options.processes, options.multiprocess,
options.chunksize, options.median)
total_duration = time.time() - start
print("%0.6f" % total_duration)
if not options.dist_only:
metrics.run_greedy_informed(data, g, apsp, options.weighted)
metrics.run_greedy_alg_dict(
data, g, 'greedy-cc', 'latency',
nx.closeness_centrality(g, weighted_edges=options.weighted), apsp,
options.weighted)
metrics.run_greedy_alg_dict(data, g, 'greedy-dc', 'latency',
nx.degree_centrality(g), apsp,
options.weighted)
for i in [10, 100, 1000]:
metrics.run_best_n(data, g, apsp, i, options.weighted)
metrics.run_worst_n(data, g, apsp, i, options.weighted)
print(
"*******************************************************************")
# Ignore the actual combinations in CSV outputs as well as single points.
exclude = ["distribution", "metric", "group", "id"]
if not options.write_combos:
exclude += ['highest_combo', 'lowest_combo']
if options.write:
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
os.mkdir(dirname)
write_json_file(filename + '.json', data)
if options.write_csv:
write_csv_file(filename, data["data"], exclude=exclude)
if options.write_dist:
write_dist_csv_file(filename + '_dist', data["data"], exclude)
return data, filename
def do_metrics(options, topo, g):
'''Compute the metrics for a single topology.'''
print "computing metricss for topo: %s" % topo
controllers = get_controllers(g, options)
filename = get_filename(topo, options, controllers)
data = {} # See top for data schema details.
apsp = nx.all_pairs_dijkstra_path_length(g)
apsp_paths = nx.all_pairs_dijkstra_path(g)
extra_params = get_extra_params(g)
if options.use_prior:
data = read_json_file(filename)
else:
start = time.time()
weighted = True
metrics.run_all_combos(options.metrics, g, controllers, data, apsp,
apsp_paths, weighted, options.write_dist,
options.write_combos, extra_params, options.processes,
options.multiprocess, options.chunksize, options.median)
total_duration = time.time() - start
print "%0.6f" % total_duration
if not options.dist_only:
metrics.run_greedy_informed(data, g, apsp, options.weighted)
metrics.run_greedy_alg_dict(data, g, 'greedy-cc', 'latency', nx.closeness_centrality(g, weighted_edges = options.weighted), apsp, options.weighted)
metrics.run_greedy_alg_dict(data, g, 'greedy-dc', 'latency', nx.degree_centrality(g), apsp, options.weighted)
for i in [10, 100, 1000]:
metrics.run_best_n(data, g, apsp, i, options.weighted)
metrics.run_worst_n(data, g, apsp, i, options.weighted)
print "*******************************************************************"
# Ignore the actual combinations in CSV outputs as well as single points.
exclude = ["distribution", "metric", "group", "id"]
if not options.write_combos:
exclude += ['highest_combo', 'lowest_combo']
if options.write:
dirname = os.path.dirname(filename)
if not os.path.exists(dirname):
os.mkdir(dirname)
write_json_file(filename + '.json', data)
if options.write_csv:
write_csv_file(filename, data["data"], exclude = exclude)
if options.write_dist:
write_dist_csv_file(filename + '_dist', data["data"], exclude)
return data, filename
else:
apsp = nx.all_pairs_shortest_path_length(g)
apsp_paths = nx.all_pairs_shortest_path(g)
if USE_PRIOR_OPTS:
data = read_json_file(PRIOR_OPTS_FILENAME)
else:
all_data = {} # data, keyed by # failures
for failures in range(1, MAX_FAILURES + 1):
# data['data'][num controllers] = [latency:latency, nodes:[best-pos node(s)]]
# data['metrics'] = [list of metrics included]
# latency is also equal to 1/closeness centrality.
all_data[failures] = {}
extra_params['max_failures'] = failures
metrics.run_all_combos(METRICS, g, controllers, all_data[failures],
apsp, apsp_paths, WEIGHTED, WRITE_DIST,
WRITE_COMBOS, extra_params)
# extract ordering of availability
extract = {} # extract[1] = data for 1 failure
failures = range(1, MAX_FAILURES + 1)
for j in failures:
extract[j] = []
print "getting data for %i failure" % j
for i, point in enumerate(all_data[j]['data'][str(1)]['distribution']):
id = point['id']
combo = point['combo']
a = point['availability']
extract[j].append([id, a, combo])
extract[j] = sorted(extract[j], key=itemgetter(1), reverse=True)
for combo in combinations(failures, 2):
print "comparing %s and %s:" % (combo[0], combo[1])
apsp_paths = nx.all_pairs_dijkstra_path(g)
else:
apsp = nx.all_pairs_shortest_path_length(g)
apsp_paths = nx.all_pairs_shortest_path(g)
if USE_PRIOR_OPTS:
data = read_json_file(PRIOR_OPTS_FILENAME)
else:
all_data = {} # data, keyed by # failures
for failures in range(1, MAX_FAILURES + 1):
# data['data'][num controllers] = [latency:latency, nodes:[best-pos node(s)]]
# data['metrics'] = [list of metrics included]
# latency is also equal to 1/closeness centrality.
all_data[failures] = {}
extra_params['max_failures'] = failures
metrics.run_all_combos(METRICS, g, controllers, all_data[failures], apsp,
apsp_paths, WEIGHTED, WRITE_DIST, WRITE_COMBOS, extra_params)
# extract ordering of availability
extract = {} # extract[1] = data for 1 failure
failures = range(1, MAX_FAILURES + 1)
for j in failures:
extract[j] = []
print "getting data for %i failure" % j
for i, point in enumerate(all_data[j]['data'][str(1)]['distribution']):
id = point['id']
combo = point['combo']
a = point['availability']
extract[j].append([id, a, combo])
extract[j] = sorted(extract[j], key = itemgetter(1), reverse = True)
for combo in combinations(failures, 2):
print "comparing %s and %s:" % (combo[0], combo[1])
left = combo[0]