def run_experiment():
network_flow_algorithm = __network_flow_algorithm(network_flow_algorithm = CURRENT_NETWORK_FLOW_ALGORITHM)
firedex_configuration = FiredexConfiguration(
network_flows = CURRENT_NETWORK_FLOWS,
priorities = 0,
network_flow_algorithm = CURRENT_NETWORK_FLOW_ALGORITHM,
priority_algorithm = "",
drop_rate_algorithm = "",
rho_tolerance = 0
)
SUBSCRIBER = "subscriber1"
HOST = "10.0.0.1"
subscriptions = []
for i in range(21):
random_subscription = __random_subscription(SUBSCRIBER, "topic")
subscriptions.append(random_subscription)
network_flows = network_flow_algorithm.apply(
firedex_configuration = firedex_configuration,
subscriber = SUBSCRIBER,
host = HOST,
subscriber_subscriptions = subscriptions
)
mean = numpy.mean( [ subscription.utility_function() for subscription in subscriptions ] )
variance = numpy.var( [ subscription.utility_function() for subscription in subscriptions ] )
intra_variance = 0
for network_flow in network_flows:
weight = float( network_flow.subscriptions().__len__() ) / float( subscriptions.__len__() )
current_variance = numpy.var( [ subscription.utility_function() for subscription in network_flow.subscriptions() ] )
intra_variance += weight * current_variance
inter_variance = 0
for network_flow in network_flows:
weight = float( network_flow.subscriptions().__len__() ) / float( subscriptions.__len__() )
current_mean = numpy.mean( [ subscription.utility_function() for subscription in network_flow.subscriptions() ] )
inter_variance += weight * ( (current_mean - mean) ** 2 )
# print("intra-variance: ", intra_variance)
# print("inter-variance: ", inter_variance)
# print( intra_variance + inter_variance, variance )
INTRA_VARIANCES.append(intra_variance)
INTER_VARIANCES.append(inter_variance)
VARIANCES.append(variance)
def push_experiment_configuration():
json_request = request.json
experiment_configuration = json.loads(json_request)
network_configuration = experiment_configuration["network_configuration"]
controller_configuration = experiment_configuration[
"controller_configuration"]
firedex_configuration = experiment_configuration["firedex_configuration"]
bandwidth = network_configuration["bandwidth"]
delay = network_configuration["delay"]
error_rate = network_configuration["error_rate"]
configuration.network_configuration = NetworkConfiguration(
bandwidth=bandwidth, delay=delay, error_rate=error_rate)
host = controller_configuration["host"]
port = controller_configuration["port"]
switch_identifier = controller_configuration["switch_identifier"]
configuration.controller_configuration = ControllerConfiguration(
host=host, port=port, switch_identifier=switch_identifier)
network_flows = firedex_configuration["network_flows"]
priorities = firedex_configuration["priorities"]
network_flow_algorithm = firedex_configuration["network_flow_algorithm"]
priority_algorithm = firedex_configuration["priority_algorithm"]
drop_rate_algorithm = firedex_configuration["drop_rate_algorithm"]
rho_tolerance = firedex_configuration["rho_tolerance"]
configuration.firedex_configuration = FiredexConfiguration(
network_flows=network_flows,
priorities=priorities,
network_flow_algorithm=__network_flow_algorithm(
network_flow_algorithm=network_flow_algorithm),
priority_algorithm=__priority_algorithm(
priority_algorithm=priority_algorithm),
drop_rate_algorithm=__drop_rate_algorithm(
drop_rate_algorithm=drop_rate_algorithm),
rho_tolerance=rho_tolerance)
configuration.publishers_configuration = PublishersConfiguration()
configuration.subscribers_configuration = SubscribersConfiguration()
return jsonify({"result": "successful"})
def run_experiment():
network_flow_algorithm = __network_flow_algorithm(
network_flow_algorithm=NETWORK_FLOW_ALGORITHM)
priority_algorithm = __priority_algorithm(
priority_algorithm=PRIORITY_ALGORITHM)
drop_rate_algorithm = __drop_rate_algorithm(
drop_rate_algorithm=CURRENT_DROP_RATE_ALGORITHM)
network_configuration = NetworkConfiguration(bandwidth=BANDWIDTH,
delay=0,
error_rate=0)
firedex_configuration = FiredexConfiguration(
network_flows=NETWORK_FLOWS,
priorities=PRIORITIES,
network_flow_algorithm=NETWORK_FLOW_ALGORITHM,
priority_algorithm=PRIORITY_ALGORITHM,
drop_rate_algorithm=CURRENT_DROP_RATE_ALGORITHM,
rho_tolerance=RHO_TOLERANCE)
TOPIC = []
for topic_index in range(1, 21 + 1):
TOPIC.append("topic%d" % topic_index)
publishers_configuration = PublishersConfiguration()
for topic in TOPIC:
publishers_configuration.publication_collection().add_publication(
publisher="publisher", topic=topic, rate=10, message_size=51)
subscriptions = []
for subscriber_index in range(SUBSCRIBERS):
subscriber = "subscriber%d" % subscriber_index
host = "10.0.0.%d" % subscriber_index
for topic_index in range(1, 21 + 1):
topic = "topic%d" % topic_index
utility_function = __random_utility_function(0.01, 100)
publication_collection = publishers_configuration.publication_collection(
)
publications_load_by_topic = publication_collection.publications_load_by_topic(
topic=topic)
adjusted_utility_function = float(utility_function) / float(
publications_load_by_topic)
random_subscription = Subscription(
subscriber=subscriber,
topic=topic,
utility_function=utility_function,
adjusted_utility_function=adjusted_utility_function)
subscriptions.append(random_subscription)
network_flows = []
for subscriber_index in range(SUBSCRIBERS):
subscriber = "subscriber%d" % subscriber_index
host = "10.0.0.%d" % subscriber_index
subscriber_subscriptions = __subscriber_subscriptions(
subscriber, subscriptions)
subscriber_network_flows = network_flow_algorithm.apply(
firedex_configuration=firedex_configuration,
subscriber=subscriber,
host=host,
subscriber_subscriptions=subscriber_subscriptions)
network_flows.extend(subscriber_network_flows)
priority_algorithm.apply(firedex_configuration=firedex_configuration,
network_flows=network_flows)
drop_rate_algorithm.apply(
network_configuration=network_configuration,
firedex_configuration=firedex_configuration,
publishers_configuration=publishers_configuration,
network_flows=network_flows)
sum_achieved_utility = 0
max_achieved_utility = 0
for network_flow in network_flows:
sum_achieved_utility += __achieved_utility(network_flow)
network_flow.set_drop_rate(0)
max_achieved_utility += __achieved_utility(network_flow)
ACHIEVED_UTILITIES.append(sum_achieved_utility)
MAX_ACHIEVED_UTILITIES.append(max_achieved_utility)
def run_experiment():
network_flow_algorithm = __network_flow_algorithm(
network_flow_algorithm=NETWORK_FLOW_ALGORITHM)
priority_algorithm = __priority_algorithm(
priority_algorithm=CURRENT_PRIORITY_ALGORITHM)
firedex_configuration = FiredexConfiguration(
network_flows=NETWORK_FLOWS,
priorities=CURRENT_PRIORITIES,
network_flow_algorithm=NETWORK_FLOW_ALGORITHM,
priority_algorithm=CURRENT_PRIORITY_ALGORITHM,
drop_rate_algorithm="",
rho_tolerance=0)
subscriptions = []
for subscriber_index in range(SUBSCRIBERS):
subscriber = "subscriber%d" % subscriber_index
host = "10.0.0.%d" % subscriber_index
for i in range(21):
random_subscription = __random_subscription(subscriber, "topic")
subscriptions.append(random_subscription)
network_flows = []
for subscriber_index in range(SUBSCRIBERS):
subscriber = "subscriber%d" % subscriber_index
host = "10.0.0.%d" % subscriber_index
subscriber_subscriptions = __subscriber_subscriptions(
subscriber, subscriptions)
subscriber_network_flows = network_flow_algorithm.apply(
firedex_configuration=firedex_configuration,
subscriber=subscriber,
host=host,
subscriber_subscriptions=subscriber_subscriptions)
network_flows.extend(subscriber_network_flows)
priority_algorithm.apply(firedex_configuration=firedex_configuration,
network_flows=network_flows)
mean = numpy.mean(
[network_flow.utility_function() for network_flow in network_flows])
variance = numpy.var(
[network_flow.utility_function() for network_flow in network_flows])
network_flows_by_priority = __network_flows_by_priority(network_flows)
intra_variance = 0
for priority in network_flows_by_priority.keys():
weight = float(network_flows_by_priority[priority].__len__()) / float(
network_flows.__len__())
current_variance = numpy.var([
network_flow.utility_function()
for network_flow in network_flows_by_priority[priority]
])
intra_variance += weight * current_variance
inter_variance = 0
for priority in network_flows_by_priority.keys():
weight = float(network_flows_by_priority[priority].__len__()) / float(
network_flows.__len__())
current_mean = numpy.mean([
network_flow.utility_function()
for network_flow in network_flows_by_priority[priority]
])
inter_variance += weight * ((current_mean - mean)**2)
# print("intra-variance: ", intra_variance)
# print("inter-variance: ", inter_variance)
# print( intra_variance + inter_variance, variance )
INTRA_VARIANCES.append(intra_variance)
INTER_VARIANCES.append(inter_variance)
VARIANCES.append(variance)
def firedex_scenario():
configuration.firedex_configuration = FiredexConfiguration()
print("Firedex configuration loaded.")