python-simpy-doc 2.3.1-1 / usr / share / doc / python-simpy-doc / html / _static / simulator.txt

# coding=utf8
"""
The fridge simulation

@author: Stefan Scherfke
@contact: stefan.scherfke at uni-oldenburg.de
"""

from time import clock
import logging

from SimPy.Simulation import Simulation, activate, initialize, simulate
import pp

from processes import Fridge, FridgeObserver

log = logging.getLogger('Simulator')

class Simulator(object):
	"""
	This class simulates a number of fridges and gets the resulting data.
	"""

	def __init__(self, numFridges, tau, aggSteps, duration):
		"""
		Setup the simulation with the specified number of fridges.

		Tau specifies the simulation step for each frige. Furthermore the
		observer will collect data each tau. Collected data
		will be aggregated at the end of each aggSteps simulation steps.

		@param numFridges: The number of simulated fridges
		@type numFridges:  unsigned int
		@param tau: simulation step size for collecting data and simulating
		            the fridge
		@type tau:  float
		@param aggSteps: Collected data will be aggregated each aggSteps
		                 simulation steps. Signals interval will be
						 tau * aggSteps
		@type aggSteps:  unsigned int
		@param duration: Duration of the simulation in hours
		@type duration:  unsigned int
		"""
		log.info('Initializing simulator ...')
		self.simEnd = duration
		self.sim = Simulation()

		fridgeProperties = {'tau': tau}
		self._fridges = []
		for i in range(numFridges):
			fridge = Fridge(self.sim, **fridgeProperties)
			self._fridges.append(fridge)
		self._observer = FridgeObserver(self.sim, self._fridges, tau, aggSteps)

	def simulate(self):
		"""
		Initialize the system, start the simulation and return the collected
		data.

		@return: The fridgerators consumption after each aggregation
		"""
		log.info('Running simulation ...')
		self.sim.initialize()
		for fridge in self._fridges:
			self.sim.activate(fridge, fridge.run(), at = 0)
		self.sim.activate(self._observer, self._observer.run(), at = 0)
		self.sim.simulate(until = self.simEnd)

		log.info('Simulation run finished.')
		return self._observer.getData()


class ParallelSimulator(object):
	"""
	This class simulates a number of fridges and gets the resulting data.
	Unlike simulator, a number of jobs will be created that use all availale
	CPU cores or even other computers.

	To use clustering, ParallelPython needs to be installed on all computers
	and the server demon "ppserver.py" must be started. The list of the server's
	IPs must then be passed to the constructor of this class.
	"""

	def __init__(self, numFridges, tau, aggSteps, duration,
			jobSize = 100, servers = ()):
		"""
		Setup the simulation with the specified number of fridges. It will be
		split up in several parallel jobs, each with the specified number of
		jobs.

		Tau specifies the simulation step for each frige. Furthermore the
		observer will collect data each tau. Collected data
		will be aggregated at the end of each aggSteps simulation steps.

		@param numFridges: The number of simulated fridges
		@type numFridges:  unsigned int
		@param tau: simulation step size for collecting data and simulating
		the fridge
		@type tau:  float
		@param aggSteps: Collected data will be aggregated each aggSteps
		simulation steps. Signals interval will be
		tau * aggSteps
		@type aggSteps:  unsigned int
		@param duration: Duration of the simulation
		@type duration:  unsigned int
		@param jobSize: The number of friges per job, defaults to 100.
		@type jobSize: unsigned int
		@param servers: A list of IPs from on which the simulation shall be
		                executed. Defaults to "()" (use only SMP)
		@type servers:  tuple of string
		"""
		log.info('Initializing prallel simulation ...')

		self._jobSize = jobSize
		self._servers = servers
		self._numFridges = numFridges
		self._tau = tau
		self._aggSteps = aggSteps
		self.simEnd = duration

	def simulate(self):
		"""
		Create some simulation jobs, run them and retrieve their results.

		@return: The fridgerators consumption after each aggregation
		"""
		log.info('Running parallel simulation ...')
		oldLevel = log.getEffectiveLevel() # pp changes the log level :(
		jobServer = pp.Server(ppservers = self._servers)

		# Start the jobs
		remainingFridges = self._numFridges
		jobs = []
		while remainingFridges > 0:
			jobs.append(jobServer.submit(self.runSimulation,
					(min(self._jobSize, remainingFridges),),
					(),
					("logging", "SimPy.Simulation", "processes")))
			remainingFridges -= self._jobSize
		log.info('Number of jobs for simulation: %d' % len(jobs))

		# Add each job's data
		pSum = [0] * int((60 / self._aggSteps) * self.simEnd)
		for job in jobs:
			data = job()
			for i in range(len(data)):
				pSum[i] += data[i]
		for s in pSum:
			s /= len(jobs)

		log.setLevel(oldLevel)
		log.info('Parallel simulation finished.')
		return pSum

	def runSimulation(self, numFridges):
		"""
		Create a job with the specified number of fridges and controllers and
		one observer. Simulate this and return the results.

		@param numFridges: The number of fridges to use for this job
		@type numFridges:  unsigned int
		@return: A list with the aggregated fridge consumption
		"""
		sim = SimPy.Simulation.Simulation()
		sim.initialize()

		fridgeProperties = {'tau': self._tau}
		fridges = []
		for i in range(numFridges):
			fridge = processes.Fridge(sim, **fridgeProperties)
			fridges.append(fridge)
			sim.activate(fridge, fridge.run(), at = 0)
		observer = processes.FridgeObserver(sim,
				fridges, self._tau, self._aggSteps)
		sim.activate(observer, observer.run(), at = 0)

		sim.simulate(until = self.simEnd)
		return observer.getData()


if __name__ == '__main__':
	logging.basicConfig(
			level = logging.INFO,
			format = '%(asctime)s %(levelname)8s: %(name)s: %(message)s')
	
	numFridges = 5000
	tau = 1./60
	aggStep = 15
	duration = 4 + tau
	
	sim = Simulator(numFridges, tau, aggStep, duration)
	data = sim.simulate()
	log.info('Results: ' + str(data))

	servers = ()
	sim = ParallelSimulator(numFridges, tau, aggStep, duration, 100, servers)
	data = sim.simulate()
	log.info('Results: ' + str(data))