Mininet Project
edoardoColi
1 year ago
parent
64c96749d6
commit
b25d1e04fc
@ -1,187 +0,0 @@
|
|||||||
import mininet
|
|
||||||
import time, socket, random
|
|
||||||
import numpy as np
|
|
||||||
from mininet.cli import CLI
|
|
||||||
from mininet.log import setLogLevel
|
|
||||||
from mininet.net import Mininet
|
|
||||||
from mininet.topo import Topo
|
|
||||||
from mininet.link import TCLink
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
|
|
||||||
# Define custom topology
|
|
||||||
class MyTopology(Topo):
|
|
||||||
def build(self):
|
|
||||||
# Create switches
|
|
||||||
s1 = self.addSwitch('s1')
|
|
||||||
s2 = self.addSwitch('s2')
|
|
||||||
|
|
||||||
# Create hosts
|
|
||||||
h1 = self.addHost('h1')
|
|
||||||
h2 = self.addHost('h2')
|
|
||||||
|
|
||||||
# Add links
|
|
||||||
self.addLink(h1, s1, cls=TCLink, delay='10ms', bw=1)
|
|
||||||
self.addLink(s1, s2, cls=TCLink, delay='50ms', bw=0.5)
|
|
||||||
self.addLink(s2, h2, cls=TCLink, delay='10ms', bw=1)
|
|
||||||
|
|
||||||
# Define TCP agent
|
|
||||||
class MyTCPAgent:
|
|
||||||
def __init__(self):
|
|
||||||
# Initialize TCP agent
|
|
||||||
self.transmission_rounds = []
|
|
||||||
self.congestion_window_sizes = []
|
|
||||||
|
|
||||||
|
|
||||||
def handle_connection(self):
|
|
||||||
print("TCP connection establishment")
|
|
||||||
# Implement TCP connection establishment
|
|
||||||
|
|
||||||
# Example TCP connection establishment using a socket
|
|
||||||
# self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
|
||||||
# self.sock.connect(('localhost', 19191))
|
|
||||||
|
|
||||||
# Example TCP connection establishment simulation
|
|
||||||
time.sleep(1) # Simulating connection establishment delay
|
|
||||||
|
|
||||||
def handle_data_transfer(self, tunnel, window_size):
|
|
||||||
print("Performing data transfer with tunnel:", tunnel, "and window size:", window_size)
|
|
||||||
# Implement TCP data transfer with the given tunnel and window size using socket programming
|
|
||||||
|
|
||||||
# Implement TCP data transfer simulation
|
|
||||||
|
|
||||||
# Placeholder logic: Simulating data transfer
|
|
||||||
time.sleep(0.1) # Simulating data transfer delay
|
|
||||||
|
|
||||||
# Simulate congestion control by waiting for a fixed amount of time
|
|
||||||
time.sleep(0.1)
|
|
||||||
|
|
||||||
# Record transmission round and final congestion window size
|
|
||||||
self.transmission_rounds.append(len(self.transmission_rounds) + 1)
|
|
||||||
self.congestion_window_sizes.append(window_size)
|
|
||||||
|
|
||||||
# Define RL agent for tunnel selection
|
|
||||||
class TunnelSelectionAgent:
|
|
||||||
def __init__(self, tunnels):
|
|
||||||
# Initialize your RL agent for tunnel selection
|
|
||||||
self.rewards = []
|
|
||||||
self.selected_tunnels = []
|
|
||||||
self.tunnels = tunnels
|
|
||||||
self.current_tunnel = 0
|
|
||||||
|
|
||||||
def select_tunnel(self):
|
|
||||||
# Implement tunnel selection based on RL policy
|
|
||||||
# Placeholder logic: Select a tunnel randomly or based on some criteria
|
|
||||||
selected_tunnel = self.tunnels[self.current_tunnel]
|
|
||||||
self.current_tunnel = (self.current_tunnel + 1) % len(self.tunnels)
|
|
||||||
return selected_tunnel
|
|
||||||
|
|
||||||
def update_policy(self, reward):
|
|
||||||
# Implement RL policy update based on rewards
|
|
||||||
self.rewards.append(reward)
|
|
||||||
self.selected_tunnels.append(self.select_tunnel())
|
|
||||||
|
|
||||||
# Define RL agent for window prediction
|
|
||||||
class WindowPredictionAgent:
|
|
||||||
def __init__(self):
|
|
||||||
# Initialize the RL agent for window prediction
|
|
||||||
self.window_sizes = [1, 2, 4, 8, 16, 32, 64] # Possible window sizes
|
|
||||||
self.alpha = 0.1 # Learning rate
|
|
||||||
self.gamma = 0.9 # Discount factor
|
|
||||||
self.q_table = {} # Q-table to store state-action values
|
|
||||||
|
|
||||||
def predict_window_size(self):
|
|
||||||
# Get the current state (e.g., network conditions)
|
|
||||||
state = self.get_state()
|
|
||||||
|
|
||||||
# Check if the state is in the Q-table
|
|
||||||
if state not in self.q_table:
|
|
||||||
# Initialize Q-values for all possible actions in the current state
|
|
||||||
self.q_table[state] = {window_size: 0 for window_size in self.window_sizes}
|
|
||||||
|
|
||||||
# Choose the action (window size) based on epsilon-greedy policy
|
|
||||||
if random.random() < 0.2: # Exploration (20% of the time)
|
|
||||||
action = random.choice(self.window_sizes)
|
|
||||||
else: # Exploitation (80% of the time)
|
|
||||||
action = self.get_best_action(state)
|
|
||||||
|
|
||||||
return action
|
|
||||||
|
|
||||||
def get_state(self):
|
|
||||||
# Implement the logic to determine the current state based on network conditions
|
|
||||||
# For example, you can consider factors such as round-trip time, packet loss rate, or congestion signals
|
|
||||||
|
|
||||||
# Placeholder logic: Return a random state
|
|
||||||
return random.randint(1, 10)
|
|
||||||
|
|
||||||
def get_best_action(self, state):
|
|
||||||
# Find the action (window size) with the highest Q-value for the given state
|
|
||||||
best_action = max(self.q_table[state], key=self.q_table[state].get)
|
|
||||||
return best_action
|
|
||||||
|
|
||||||
def update_policy(self, reward):
|
|
||||||
# Update the Q-value based on the reward received after taking an action
|
|
||||||
# Get the previous state and action
|
|
||||||
prev_state = self.get_state() # Replace with the actual previous state
|
|
||||||
prev_action = self.predict_window_size() # Replace with the actual previous action
|
|
||||||
|
|
||||||
# Get the current state
|
|
||||||
curr_state = self.get_state()
|
|
||||||
|
|
||||||
# Check if the current state is in the Q-table
|
|
||||||
if curr_state not in self.q_table:
|
|
||||||
# Initialize Q-values for all possible actions in the current state
|
|
||||||
self.q_table[curr_state] = {window_size: 0 for window_size in self.window_sizes}
|
|
||||||
|
|
||||||
# Update the Q-value using the Q-learning update rule
|
|
||||||
max_q_value = max(self.q_table[curr_state].values()) # Get the maximum Q-value for the current state
|
|
||||||
self.q_table[prev_state][prev_action] += self.alpha * (reward + self.gamma * max_q_value - self.q_table[prev_state][prev_action])
|
|
||||||
|
|
||||||
# Create the Mininet network with custom topology
|
|
||||||
def create_network():
|
|
||||||
topo = MyTopology()
|
|
||||||
net = Mininet(topo=topo)
|
|
||||||
net.start()
|
|
||||||
return net
|
|
||||||
|
|
||||||
# Main function
|
|
||||||
if __name__ == '__main__':
|
|
||||||
setLogLevel('info')
|
|
||||||
|
|
||||||
# Create the Mininet network
|
|
||||||
net = create_network()
|
|
||||||
|
|
||||||
# Instantiate TCP and RL agents
|
|
||||||
tcp_agent = MyTCPAgent()
|
|
||||||
tunnels = ['myPrivate1']
|
|
||||||
tunnel_agent = TunnelSelectionAgent(tunnels)
|
|
||||||
window_agent = WindowPredictionAgent()
|
|
||||||
|
|
||||||
# Perform TCP connection establishment
|
|
||||||
tcp_agent.handle_connection()
|
|
||||||
|
|
||||||
# Perform data transfer with RL-based tunnel selection and window prediction
|
|
||||||
start_time = time.time()
|
|
||||||
|
|
||||||
while True:
|
|
||||||
if time.time() - start_time > 10: # End packet exchange after <X> seconds
|
|
||||||
break
|
|
||||||
tunnel = tunnel_agent.select_tunnel()
|
|
||||||
window_size = window_agent.predict_window_size()
|
|
||||||
|
|
||||||
# Perform data transfer
|
|
||||||
tcp_agent.handle_data_transfer(tunnel, window_size)
|
|
||||||
|
|
||||||
# Update RL agents based on rewards
|
|
||||||
reward = 0.5 # Actual reward value
|
|
||||||
tunnel_agent.update_policy(reward)
|
|
||||||
# window_agent.update_policy(reward)
|
|
||||||
|
|
||||||
# Stop the Mininet network
|
|
||||||
net.stop()
|
|
||||||
|
|
||||||
# Plot Transmission Round versus Congestion Window Size
|
|
||||||
plt.plot(tcp_agent.transmission_rounds, tcp_agent.congestion_window_sizes)
|
|
||||||
plt.xlabel('Transmission Round')
|
|
||||||
plt.ylabel('Congestion Window Size')
|
|
||||||
plt.title('TCP+RL Window Prediction')
|
|
||||||
plt.show()
|
|
||||||
Loading…
Reference in New Issue