AI in NOC: A New Era for Capacity Planning and Network Management

 

 Introduction :-

Predicting router interface traffic in modern network management is critical to achieve optimal network performance, resource allocation, and proactively eliminate bottlenecks. This case study suggests using machine learning on network datasets to develop a model that predicts an actual router’s interface traffic versus an ideal router’s traffic. By evaluating historical data of network traffic and utilizing well-established machine learning API-based algorithms like Neural Networks, Decision Trees, and Ensemble Methods, this analysis reviews the formation possibilities of a strong prediction model with grand accuracy. At the end of the study, this paper reviews multiple factors and features related to the traffic in the interface such as the hour in the day, the day’s sequence in the week, the network protocol, the application, and the structure of the network. Additionally, it explores the machine learning algorithms to compare the accuracy of the ML models against the different interface traffic predictions. Overall, the results of this study will propose working predictive modelling systems for router interface traffic to improve current performance strategies. 

import csv
import random
from datetime import datetime, timedelta

# Function to generate random data for the past year
def generate_data(start_date, end_date):
    current_date = start_date
    data = []
    while current_date <= end_date:
        cpu_utilization = round(random.uniform(0, 100), 2)  # Random CPU utilization
        memory_utilization = round(random.uniform(0, 100), 2)  # Random memory utilization
        interface_traffic = round(random.uniform(0, 1000), 2)  # Random interface traffic
        temperature = round(random.uniform(20, 40), 2)  # Random temperature
        data.append([current_date.strftime('%Y-%m-%d'), cpu_utilization, memory_utilization, interface_traffic, temperature])
        current_date += timedelta(days=1)
    return data

# Define start and end dates for the past year
start_date = datetime.now() - timedelta(days=365)
end_date = datetime.now()

# Generate data
data = generate_data(start_date, end_date)

# Write data to CSV file
with open('router_switch_data.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Date', 'CPU Utilization (%)', 'Memory Utilization (%)', 'Interface Traffic (Mbps)', 'Temperature (C)'])
    writer.writerows(data)

print("CSV file 'router_switch_data.csv' generated successfully!")

CSV file 'router_switch_data.csv' generated successfully!

 

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# Load the dataset
data = pd.read_csv('router_switch_data.csv')

# Convert 'Date' column to datetime
data['Date'] = pd.to_datetime(data['Date'])

# Extract features
X = data['Date'].dt.dayofyear.values.reshape(-1, 1) # Using day of the year as a feature
y = data['Interface Traffic (Mbps)'].values

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions
y_pred_train = model.predict(X_train)
y_pred_test = model.predict(X_test)

# Calculate RMSE
rmse_train = mean_squared_error(y_train, y_pred_train, squared=False)
rmse_test = mean_squared_error(y_test, y_pred_test, squared=False)
print(f"Train RMSE: {rmse_train:.2f}")
print(f"Test RMSE: {rmse_test:.2f}")

# Plot actual vs. predicted values
plt.figure(figsize=(10, 6))
plt.scatter(data['Date'], data['Interface Traffic (Mbps)'], color='blue', label='Actual')
plt.scatter(data['Date'], model.predict(X), color='red', label='Predicted')
plt.title('Actual vs. Predicted Interface Traffic')
plt.xlabel('Date')
plt.ylabel('Interface Traffic (Mbps)')
plt.legend()
plt.show()
 

                                                  Fig : Actual Traffic Vs Predicted Traffic router interface

 

Using AI for Better Network Management in NOCs

  Capacity planning using AI in network operations centers (NOCs) involves leveraging advanced machine learning algorithms and data analytics to predict and manage network traffic and resource needs. By analyzing historical data, real-time metrics, and user behavior patterns, AI can forecast network demand with greater accuracy, helping to prevent congestion and optimize resource allocation. This predictive capability allows NOCs to proactively adjust infrastructure, such as scaling bandwidth or deploying additional servers, to maintain optimal performance and avoid potential outages. Additionally, AI can identify emerging trends and anomalies, enabling faster response to unexpected changes in network conditions. Overall, AI-driven capacity planning enhances the efficiency, reliability, and scalability of network management.