def apply(self, actions: SimulatorAction):
self.writer.write_action_result(self.env, actions)
# increase performance when debug logging is disabled
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f"SimulatorAction: %s", repr(actions))
# Get the new placement from the action passed by the RL agent
# Modify and set the placement parameter of the instantiated simulator object.
self.simulator.params.sf_placement = actions.placement
# Update which sf is available at which node
for node_id, placed_sf_list in actions.placement.items():
available = {}
# Keep only SFs which still process
for sf, sf_data in self.simulator.params.network.nodes[node_id][
'available_sf'].items():
if sf_data['load'] != 0:
available[sf] = sf_data
# Add all SFs which are in the placement
for sf in placed_sf_list:
available[sf] = available.get(sf, {'load': 0.0})
self.simulator.params.network.nodes[node_id][
'available_sf'] = available
# Get the new schedule from the SimulatorAction
# Set it in the params of the instantiated simulator object.
self.simulator.params.schedule = actions.scheduling
# reset metrics for steps
metrics.reset_run()
# Run the simulation again with the new params for the set duration.
# Due to SimPy restraints, we multiply the duration by the run times because SimPy does not reset when run()
# stops and we must increase the value of "until=" to accomodate for this. e.g.: 1st run call runs for 100 time
# uniits (1 run time), 2nd run call will also run for 100 more time units but value of "until=" is now 200.
runtime_steps = self.duration * self.run_times
logger.debug("Running simulator until time step %s", runtime_steps)
self.env.run(until=runtime_steps)
# Parse the NetworkX object into a dict format specified in SimulatorState. This is done to account
# for changing node remaining capacities.
# Also, parse the network stats and prepare it in SimulatorState format.
self.parse_network()
self.network_metrics()
# Increment the run times variable
self.run_times += 1
# Record end time of the apply round, doesn't change start time to show the running time of the entire
# simulation at the end of the simulation.
self.end_time = time.time()
metrics.running_time(self.start_time, self.end_time)
# Create a new SimulatorState object to pass to the RL Agent
simulator_state = SimulatorState(self.network_dict,
self.simulator.params.sf_placement,
self.sfc_list, self.sf_list,
self.traffic, self.network_stats)
self.writer.write_state_results(self.env, simulator_state)
return simulator_state
def main():
args = parse_args()
metrics.reset()
start_time = time.time()
logging.basicConfig(level=logging.INFO)
# Create a SimPy environment
env = simpy.Environment()
# Seed the random generator
random.seed(args.seed)
numpy.random.seed(args.seed)
# Parse network and get NetworkX object and ingress network list
network, ing_nodes = reader.read_network(args.network,
node_cap=10,
link_cap=10)
# Getting current SFC list, and the SF list of each SFC, and config
# use dummy placement and schedule for running simulator without algorithm
# TODO: make configurable via CLI
sf_placement = dummy_data.triangle_placement
schedule = dummy_data.triangle_schedule
# Getting current SFC list, and the SF list of each SFC, and config
sfc_list = reader.get_sfc(args.sf)
sf_list = reader.get_sf(args.sf, args.sfr)
config = reader.get_config(args.config)
# Create the simulator parameters object with the provided args
params = SimulatorParams(network,
ing_nodes,
sfc_list,
sf_list,
config,
args.seed,
sf_placement=sf_placement,
schedule=schedule)
log.info(params)
if args.trace:
trace = reader.get_trace(args.trace)
TraceProcessor(params, env, trace)
# Create a FlowSimulator object, pass the SimPy environment and params objects
simulator = FlowSimulator(env, params)
# Start the simulation
simulator.start()
# Run the simpy environment for the specified duration
env.run(until=args.duration)
# Record endtime and running_time metrics
end_time = time.time()
metrics.running_time(start_time, end_time)
# dump all metrics
log.info(metrics.metrics)
def init(self,
network_file,
service_functions_file,
config_file,
seed,
trace=None,
resource_functions_path=""):
# Initialize metrics, record start time
metrics.reset()
self.run_times = int(1)
self.start_time = time.time()
# Parse network and SFC + SF file
self.network, self.ing_nodes = reader.read_network(network_file,
node_cap=10,
link_cap=10)
self.sfc_list = reader.get_sfc(service_functions_file)
self.sf_list = reader.get_sf(service_functions_file,
resource_functions_path)
self.config = reader.get_config(config_file)
# Generate SimPy simulation environment
self.env = simpy.Environment()
# Instantiate the parameter object for the simulator.
self.params = SimulatorParams(self.network, self.ing_nodes,
self.sfc_list, self.sf_list, self.config,
seed)
# Trace handling
if trace:
trace = reader.get_trace(trace)
TraceProcessor(self.params, self.env, trace)
self.duration = self.params.run_duration
# Get and plant random seed
self.seed = seed
random.seed(self.seed)
numpy.random.seed(self.seed)
# Instantiate a simulator object, pass the environment and params
self.simulator = FlowSimulator(self.env, self.params)
# Start the simulator
self.simulator.start()
# Run the environment for one step to get initial stats.
self.env.step()
# Parse the NetworkX object into a dict format specified in SimulatorState. This is done to account
# for changing node remaining capacities.
# Also, parse the network stats and prepare it in SimulatorState format.
self.parse_network()
self.network_metrics()
# Record end time and running time metrics
self.end_time = time.time()
metrics.running_time(self.start_time, self.end_time)
simulator_state = SimulatorState(self.network_dict,
self.simulator.params.sf_placement,
self.sfc_list, self.sf_list,
self.traffic, self.network_stats)
# self.writer.write_state_results(self.env, simulator_state)
return simulator_state
def init(self, seed):
# reset network caps and available SFs:
reader.reset_cap(self.network)
# Initialize metrics, record start time
metrics.reset_metrics()
self.run_times = int(1)
self.start_time = time.time()
# Parse network and SFC + SF file
# Generate SimPy simulation environment
self.env = simpy.Environment()
self.params = SimulatorParams(self.network, self.ing_nodes,
self.sfc_list, self.sf_list, self.config)
# Instantiate the parameter object for the simulator.
if self.params.use_states and 'trace_path' in self.config:
logger.warning(
'Two state model and traces are both activated, thi will cause unexpected behaviour!'
)
if self.params.use_states:
if self.params.in_init_state:
self.params.in_init_state = False
else:
self.params.update_state()
self.duration = self.params.run_duration
# Get and plant random seed
self.seed = seed
random.seed(self.seed)
numpy.random.seed(self.seed)
# Instantiate a simulator object, pass the environment and params
self.simulator = FlowSimulator(self.env, self.params)
# Start the simulator
self.simulator.start()
# Trace handling
if 'trace_path' in self.config:
trace_path = os.path.join(os.getcwd(), self.config['trace_path'])
trace = reader.get_trace(trace_path)
TraceProcessor(self.params, self.env, trace, self.simulator)
# Run the environment for one step to get initial stats.
self.env.step()
# Parse the NetworkX object into a dict format specified in SimulatorState. This is done to account
# for changing node remaining capacities.
# Also, parse the network stats and prepare it in SimulatorState format.
self.parse_network()
self.network_metrics()
# Record end time and running time metrics
self.end_time = time.time()
metrics.running_time(self.start_time, self.end_time)
simulator_state = SimulatorState(self.network_dict,
self.simulator.params.sf_placement,
self.sfc_list, self.sf_list,
self.traffic, self.network_stats)
logger.debug(f"t={self.env.now}: {simulator_state}")
return simulator_state