A M/M/1/K queue Python3 simulator that compares the simulation results (QSim.py module) against the analytics results (QAnalyic.py module). The queue have limited capacity K and processes may be blocked (if queue is full) or leave queue before get service (there is a deadline for each process) or get service from server.
- Compute probability of process blocking or leaving in a limited queue in analytic and simulation ways
import QAnalytic
import QSim
if __name__ == "__main__":
# set parameters
mu = 1 # service rate
theta = 2 # deadline rate or deadline time
lambdas = 5 # arrival rate of customers
num_customers = 10 ** 6 #number of customers in simulation
queue_size = 12 #size of queue
is_exp_drop = True # or False
simulator = QSim.simulator(lambda_,
mu,
theta,
queue_size,
is_exp_drop)
analyzer = QAnalytic.analyzer(lambda_,
mu,
theta,
queue_size,
is_exp_drop)
sim_pb, sim_pd = simulator.run(num_customers)
anal_pb, anal_pd = analyzer.run()
print(f"is_exp_drop:{is_exp_drop}, lambda:{lambda_}, mu:{mu}, theta:{theta} => simulation pb:{sim_pb}, analytic pb:{anal_pb}, simulation pd:{sim_pd}, analytic pd:{anal_pd} \n")| QSim and QAnalytic |
|---|
| lambda_ : arrival rate of customers |
| mu: server rate |
| theta: deadline rate or deadline time |
| size: size of the Queue (K) |
| is_exp_drop: Is deadline time follow a exponential distribution? or not (Fixed)? |
| num_customers: number of customers |
GNU General Public License v3.0