Inside Optus NOC: Using Predictive AI to Manage Network Load During Festive Rush

Abstract 

Predictive AI for network capacity planning uses machine learning algorithms and prior network performance to predict future needs. This ensures early detection and planning for future needs, thus planning resources ahead of demand. After processing traffic patterns, utilization statistics, and user patterns, the AI models are able to predict potential bottlenecks, the need for more bandwidth, or when to scale up, among other factors. All these are vital decisions that should be made before the congestion occurs. The use of this AI model thus helps plan capacity and balance workloads well, prevent downtime, and save overall costs. In all, it ultimately changes the capacity planning strategy from reactive to continuous, preventative maintenance to ensure workloads run perfectly at the lowest cost possible.

How Predictive AI Helps in Capacity Planning C

Capacity planning is a matter of making sure that IT infrastructure, servers, networks, bandwidth, storage, etc., would be enough to meet the future demand without having to over- or under-provision. Predictive AI makes this process intelligent and proactive:

1. Demand Forecasting

   Predictive AI can analyze usage patterns, CPU, memory, bandwidth, user sessions, etc., to foresee a surging requisite. For instance, at the current rate, bandwidth usage patterns show that it would be twenty percent higher in the next quarter. This way, it would be possible to allocate enough before congestion, or downgraded service happens. 

   2. Proactive Scaling

 Rather than reactive scaling after a specific resource has expired, Predictive AI supports auto-scaling based forecasting. For example, in the cloud, AI can determine a high rise in traffic and consequently auto-increase the number of compute instances. 

3. Cost Optimization 

By predicting usage, AI prevents two wastages, over-provisioning and under-provisioning. For example, I have computed that my website will not have a lot of traffic in the next several hours, and hence I should downscale. 

4. Anomaly & Failure Prediction Predictive models 

These models can report anomalies indicating upcoming exploits. It is done using disk correlates such as temperatures or I/O patterns. 

5. Long-Term Capacity Strategy

Predictive AI uses data to offer guidance on upcoming capacity deficiencies, expansions, and decommissions. For instance, AI forecasts that my network core routers will touch more than 85% utilization, necessitating my procurement. 

6. Improved SLA and Customer Experience 

These performance levels in computing are maintained at optimal levels without human direct involvement. For example, service providers strike specific performance deals without becoming the best.

Let’s deep and dive with understanding on scenario – 

Scenario --With the Christmas season just around the corner, Nick is a senior engineer at Optus and have been studying the core router interface record for the past three years to forecast the optimal allocation of capacity. With the help of predictive AI, his analysis guarantees the accuracy of the traffic tendency in the following period, locates the expected congestion spots, and adopts capacity-allocation plans to make appropriate changes as soon as possible to alleviate the congestion and guarantee the flawless performance in the busy season.

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()

Conclusion:

Artificial intelligence -driven capacity planning in NOCs applies advanced machine learning algorithms and data analytics to predict and regulate network traffic and associated resource requirements. AI accomplishes rigorous forecasting of network capacity demands through analysis of historical data, real-time metrics, and user activity to reduce “traffic jams” and maximize resource efficiency. By using this information to emit indicators in advance, NOCs are able to plan accordingly, such as increase or decrease bandwidth, incorporate additional servers, to continue optimal functioning and avert catastrophe. AI likewise identifies patterns and outliers allowing for quick adjustment to unforeseen changes. In sum, capacity planning with AI improves network management in terms of efficiency, reliability, and scalability.