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