def exec_experiment(topology, workload, netconf, strategy, cache_policy,
collectors):
"""Execute the simulation of a specific scenario.
Parameters
----------
topology : Topology
The FNSS Topology object modelling the network topology on which
experiments are run.
workload : iterable
An iterable object whose elements are (time, event) tuples, where time
is a float type indicating the timestamp of the event to be executed
and event is a dictionary storing all the attributes of the event to
execute
netconf : dict
Dictionary of attributes to inizialize the network model
strategy : tree
Strategy definition. It is tree describing the name of the strategy
to use and a list of initialization attributes
cache_policy : tree
Cache policy definition. It is tree describing the name of the cache
policy to use and a list of initialization attributes
collectors: dict
The collectors to be used. It is a dictionary in which keys are the
names of collectors to use and values are dictionaries of attributes
for the collector they refer to.
Returns
-------
results : Tree
A tree with the aggregated simulation results from all collectors
"""
netconf['n_contents'] = workload.n_contents
model = NetworkModel(topology, cache_policy, **netconf)
view = NetworkView(model)
controller = NetworkController(model)
collectors_inst = [
DATA_COLLECTOR[name](view, **params)
for name, params in collectors.items()
]
collector = CollectorProxy(view, collectors_inst)
controller.attach_collector(collector)
strategy_name = strategy['name']
strategy_args = {k: v for k, v in strategy.items() if k != 'name'}
strategy_inst = STRATEGY[strategy_name](view, controller, **strategy_args)
i = 0
for time, event in workload:
strategy_inst.process_event(time, **event)
# i+=1
# if not i%30:
# results = collector.results()
# logger.info('Mean hit ratio: %f | Mean latency: %f | Reward: %f.'%(results['CACHE_HIT_RATIO']['MEAN'], results['LATENCY']['MEAN'], controller.get_avg_reward()))
# workload.reward = controller.get_avg_reward()
return collector.results()
def exec_experiment(topology, workload, netconf, strategy, cache_policy, collectors, desc):
"""Execute the simulation of a specific scenario.
Parameters
----------
topology : Topology
The FNSS Topology object modelling the network topology on which
experiments are run.
workload : iterable
An iterable object whose elements are (time, event) tuples, where time
is a float type indicating the timestamp of the event to be executed
and event is a dictionary storing all the attributes of the event to
execute
netconf : dict
Dictionary of attributes to inizialize the network model
strategy : tree
Strategy definition. It is tree describing the name of the strategy
to use and a list of initialization attributes
cache_policy : tree
Cache policy definition. It is tree describing the name of the cache
policy to use and a list of initialization attributes
collectors: dict
The collectors to be used. It is a dictionary in which keys are the
names of collectors to use and values are dictionaries of attributes
for the collector they refer to.
Returns
-------
results : Tree
A tree with the aggregated simulation results from all collectors
"""
model = NetworkModel(topology, cache_policy, **netconf)
view = NetworkView(model)
controller = NetworkController(model)
collectors_inst = [DATA_COLLECTOR[name](view, **params)
for name, params in list(collectors.items())]
collector = CollectorProxy(view, collectors_inst)
controller.attach_collector(collector)
strategy_name = strategy['name']
strategy_args = {k: v for k, v in list(strategy.items()) if k != 'name'}
strategy_inst = STRATEGY[strategy_name](view, controller, **strategy_args)
processed_events = 0
for time, event in workload:
strategy_inst.process_event(time, **event)
processed_events += 1
if processed_events % 1000000 == 0:
logger.info('Progress: %s, %f' % (desc, float(processed_events) / float(workload.n_measured)))
return collector.results()
def exec_experiment(topology, workload, netconf, strategy, cache_policy, collectors, desc):
"""Execute the simulation of a specific scenario.
Parameters
----------
topology : Topology
The FNSS Topology object modelling the network topology on which
experiments are run.
workload : iterable
An iterable object whose elements are (time, event) tuples, where time
is a float type indicating the timestamp of the event to be executed
and event is a dictionary storing all the attributes of the event to
execute
netconf : dict
Dictionary of attributes to inizialize the network model
strategy : tree
Strategy definition. It is tree describing the name of the strategy
to use and a list of initialization attributes
cache_policy : tree
Cache policy definition. It is tree describing the name of the cache
policy to use and a list of initialization attributes
collectors: dict
The collectors to be used. It is a dictionary in which keys are the
names of collectors to use and values are dictionaries of attributes
for the collector they refer to.
Returns
-------
results : Tree
A tree with the aggregated simulation results from all collectors
"""
model = NetworkModel(topology, cache_policy, **netconf)
view = NetworkView(model)
controller = NetworkController(model)
collectors_inst = [DATA_COLLECTOR[name](view, **params)
for name, params in collectors.items()]
collector = CollectorProxy(view, collectors_inst)
controller.attach_collector(collector)
strategy_name = strategy['name']
strategy_args = {k: v for k, v in strategy.items() if k != 'name'}
strategy_inst = STRATEGY[strategy_name](view, controller, **strategy_args)
processed_events = 0
for time, event in workload:
strategy_inst.process_event(time, **event)
processed_events += 1
if processed_events % 1000000 == 0:
logger.info('Progress: %s, %f' % (desc, float(processed_events) / float(workload.n_measured)))
return collector.results()
def exec_experiment(topology, workload, netconf, strategy, cache_policy,
collectors):
"""Execute the simulation of a specific scenario.
Parameters
----------
topology : Topology
The FNSS Topology object modelling the network topology on which
experiments are run.
workload : iterable
An iterable object whose elements are (time, event) tuples, where time
is a float type indicating the timestamp of the event to be executed
and event is a dictionary storing all the attributes of the event to
execute
netconf : dict
Dictionary of attributes to inizialize the network model
strategy : tree
Strategy definition. It is tree describing the name of the strategy
to use and a list of initialization attributes
cache_policy : tree
Cache policy definition. It is tree describing the name of the cache
policy to use and a list of initialization attributes
collectors: dict
The collectors to be used. It is a dictionary in which keys are the
names of collectors to use and values are dictionaries of attributes
for the collector they refer to.
Returns
-------
results : Tree
A tree with the aggregated simulation results from all collectors
"""
model = NetworkModel(topology, cache_policy, **netconf)
view = NetworkView(model)
controller = NetworkController(model)
collectors_inst = [
DATA_COLLECTOR[name](view, **params)
for name, params in collectors.items()
]
collector = CollectorProxy(view, collectors_inst)
controller.attach_collector(collector)
strategy_name = strategy['name']
strategy_args = {k: v for k, v in strategy.items() if k != 'name'}
strategy_inst = STRATEGY[strategy_name](view, controller, **strategy_args)
for time, event in workload: # e.g. time: 1520036461364, event: {'receiver': 'battery3/receiver', 'content': '/agent4/battery3/charging/v0', 'log': True}
# print('calling process_event with time:', time, ' event', event)
# write_to_file(time)
strategy_inst.process_event(time, **event)
return collector.results()
def exec_experiment(topology, events, strategy, collectors):
"""
Execute the simulation of a specific scenario
Parameters
----------
topology : Topology
An FNSS topology object with the network topology used for the
simulation
events : iterable
An iterable object whose elements are (time, event) tuples, where time
is a float type indicating the timestamp of the event to be executed
and event is a dictionary storing all the attributes of the event to
execute
strategy : 2-tuple
Strategy definition. It is a 2-tuple where the first element is the
name of the strategy and the second element is a dictionary of
strategy attributes
collectors: list of tuples
The collectors to be used. It is a list of 2-tuples. Each tuple has as
first element the name of the collector and as second element a
dictionary of collector parameters
Returns
-------
results : dict
A dictionary with the aggregated simulation results from all collectors.
"""
model = NetworkModel(topology)
view = NetworkView(model)
controller = NetworkController(model)
collectors_inst = [data_collector_register[name](view, **params)
for name, params in collectors]
collector = CollectorProxy(view, collectors_inst)
controller.attach_collector(collector)
str_name, str_params = strategy
strategy_inst = strategy_register[str_name](view, controller, **str_params)
for time, event in events:
strategy_inst.process_event(time, **event)
return collector.results()
def exec_experiment(topology,
workload,
netconf,
strategy,
cache_policy,
repo_policy,
collectors,
warmup_strategy,
sched_policy={'name': 'EDF'}):
"""Execute the simulation of a specific scenario.
Parameters
----------
topology : Topology
The FNSS Topology object modelling the network topology on which
experiments are run.
workload : iterable
An iterable object whose elements are (time, event) tuples, where time
is a float type indicating the timestamp of the event to be executed
and event is a dictionary storing all the attributes of the event to
execute
netconf : dict
Dictionary of attributes to inizialize the network model
strategy : tree
Strategy definition. It is tree describing the name of the strategy
to use and a list of initialization attributes
cache_policy : tree
Cache policy definition. It is tree describing the name of the cache
policy to use and a list of initialization attributes
collectors: dict
The collectors to be used. It is a dictionary in which keys are the
names of collectors to use and values are dictionaries of attributes
for the collector they refer to.
Returns
-------
results : Tree
A tree with the aggregated simulation results from all collectors
"""
model = NetworkModel(topology, cache_policy, repo_policy,
sched_policy['name'], workload.n_services,
workload.rate, **netconf)
workload.model = model
view = NetworkView(model)
controller = NetworkController(model)
collectors_inst = [
DATA_COLLECTOR[name](view, **params)
for name, params in collectors.items()
]
collector = CollectorProxy(view, collectors_inst)
controller.attach_collector(collector)
strategy_name = strategy['name']
warmup_strategy_name = warmup_strategy['name']
strategy_args = {k: v for k, v in strategy.items() if k != 'name'}
warmup_strategy_args = {
k: v
for k, v in warmup_strategy.iteritems() if k != 'name'
}
strategy_inst = STRATEGY[strategy_name](view, controller, **strategy_args)
warmup_strategy_inst = STRATEGY[warmup_strategy_name](
view, controller, **warmup_strategy_args)
n = 0
for time, event in workload:
#continue
strategy_inst.process_event(time, **event)
if n % 500 == 0 and n:
collector.results()
if event['status'] == 1:
n += 1
return collector.results()
"""