Segment Routing: A Game-Changer for Predicting Network Latency

 

What is Segment Routing (SR)?

Segment Routing (SR) is a new way of routing that simplifies traffic engineering by encoding the entire path a packet must take through the network into the packet header. Instead of using traditional per-hop signaling protocols like MPLS-TE (Traffic Engineering), Segment Routing uses a series of segments – pre-defined instructions like visit a specific node, traverse a specific link or apply a specific service. These segments are represented as SIDs and are processed sequentially by the routers in the path. SR brings scalability as it eliminates the need to maintain complex state information in every router and flexibility to choose the path. This is particularly useful in Software-Defined Networking (SDN) environments where SR can dynamically optimize traffic flows, reduce congestion and latency. By using SR, network operators can achieve traffic steering, better resource utilization and simpler operations while meeting performance requirements.

Scenario: Oracle company need to route traffic at and from multiple routers such that they can avoid congestion and have minimum latency from source (S) to destination (D). In Segment Routing, this is done by dynamically choosing the optimal path and assigning the SR labels.   Predict Latency in Network using Gen AI technique It helps in Automation process.

To create a program that measures and analyses latency across multiple routers (R1 to R6), we can simulate this scenario using Python. This program will:

1. Define routers R1 to R6.

2. Simulate latency values between them.

3. Calculate average, minimum, and maximum latency for the network.

import pandas as pd

# Traffic data (latency in ms, bandwidth utilization in normalized values)

data = {

    "Source": ["R1", "R1", "R2", "R2", "R3", "R3", "R4", "R4", "R5", "R5"],

    "Destination": ["R2", "R3", "R3", "R4", "R4", "R5", "R5", "R6", "R6", "R1"],

    "Latency": [10, 20, 15, 25, 10, 30, 20, 15, 25, 35],

    "Bandwidth_Utilization": [0.3, 0.7, 0.4, 0.6, 0.2, 0.5, 0.3, 0.4, 0.6, 0.7],

}

# Create a DataFrame

traffic_df = pd.DataFrame(data)

# Display the DataFrame

print("Traffic Data Between Routers:")

print(traffic_df)

# Analyze or visualize the data if needed

print("\nSummary Statistics:")

print(traffic_df.describe())

Traffic Data Between Routers:
  Source Destination  Latency  Bandwidth_Utilization
0     R1          R2       10                    0.3
1     R1          R3       20                    0.7
2     R2          R3       15                    0.4
3     R2          R4       25                    0.6
4     R3          R4       10                    0.2
5     R3          R5       30                    0.5
6     R4          R5       20                    0.3
7     R4          R6       15                    0.4
8     R5          R6       25                    0.6
9     R5          R1       35                    0.7

Summary Statistics:
        Latency  Bandwidth_Utilization
count  10.00000              10.000000
mean   20.50000               0.470000
std     8.31665               0.176698
min    10.00000               0.200000
25%    15.00000               0.325000
50%    20.00000               0.450000
75%    25.00000               0.600000
max    35.00000               0.700000

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Feature selection
X = traffic_df[["Latency", "Bandwidth_Utilization"]]
y = traffic_df["Latency"]  # Predicting future latency as a proxy for congestion

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)

# Predict future latency
y_pred = model.predict(X_test)
print(f"Prediction Error (MSE): {mean_squared_error(y_test, y_pred)}")

Prediction Error (MSE): 4.066250000000004

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Plot actual vs. predicted values
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, alpha=0.7, color="blue", label="Predicted vs Actual")
plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color="red", linewidth=2, label="Ideal Prediction Line")
plt.title("Actual vs Predicted Latency")
plt.xlabel("Actual Latency")
plt.ylabel("Predicted Latency")
plt.legend()
plt.grid(True)
plt.show()

# Residual Plot

residuals = y_test - y_pred

plt.figure(figsize=(10, 6))

sns.histplot(residuals, kde=True, bins=20, color="purple", alpha=0.7)

plt.title("Residuals Distribution")

plt.xlabel("Residual (Actual - Predicted)")

plt.ylabel("Frequency")

plt.grid(True)

plt.show()

Wrap-up:

Segment Routing (SR) makes it easy to predict and monitor latency across network nodes by putting path information directly in the packet headers. In an SR network, packets carry a list of segments which define the explicit path they must take through the network. This allows network operators to measure and predict latency for specific paths without complex signaling protocols. By defining segments as specific links, nodes or services, SR allows traffic to take predictable and controlled paths and avoid congested or high latency areas. SR also integrates with telemetry systems to collect real time performance metrics like latency. This data can be used to model and forecast latency for different nodes or paths, to do proactive traffic engineering, SLA management and network performance optimization.