A Markov Chain Implementation to create a controller priority list
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#!/usr/bin/python | |
""" | |
This is a program that implements the Markov Chain algorithm | |
to calculate the next best possible controller to contact for | |
executing the source and destination flow modification. | |
""" | |
import scipy.linalg | |
import numpy as np | |
# Random Walk | |
def random_walk(state, A): | |
n = 10 | |
start_state = 0 | |
prev_state = start_state | |
while n>1: | |
curr_state = np.random.choice([0, 1, 2], p=A[prev_state]) | |
prev_state = curr_state | |
n -= 1 | |
values, left = scipy.linalg.eig(A, right=False, left=True) | |
# Normalized values of the eigen vectors | |
pi = left[:,0] | |
pi_normalized = [(x/np.sum(pi)).real for x in pi] | |
return pi_normalized | |
def controller_priority(): | |
state = { | |
0: "ODL", | |
1: "Ryu", | |
2: "FL" | |
} | |
A = np.array([[0.2, 0.15, 0.65], [0.7, 0.2, 0.1], [0.9, 0.05, 0.05]]) | |
stats = random_walk(state, A) | |
print(stats) | |
res_controllers = [state[stats.index(x)] for x in sorted(stats)][::-1] | |
return res_controllers | |
if __name__ == "__main__": | |
controller_priority() |
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