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@dat-adi
Created December 16, 2022 10:14
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A Markov Chain Implementation to create a controller priority list
#!/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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