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]) # Main function if __name__ == '__main__': setLogLevel('info') # Create the Mininet network topo = MyTopology() net = Mininet(topo=topo) net.start # 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 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 and 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()