Intelligent Traffic Engineering: Using Segment Routing and AI to Avoid Congestion in Next-Gen Networks

By Admin | 07-12-2025

Insight:

Latency is crucial for application performance, especially for real-time services like VoIP, video meetings, online trading, telemedicine, payment gateways, industrial IoT, and cloud-hosted applications. Even a few milliseconds of delay can hurt user experience, causing issues like delays, jitter, or timeouts. MPLS Traffic Engineering (MPLS-TE) helps these applications work smoothly by reserving bandwidth, selecting low-latency paths, and proactively avoiding congested links through RSVP-TE engineered LSPs. This allows network operators to meet strict SLAs, including maximum latency, minimal packet loss, and near-zero downtime, while prioritizing essential traffic. By routing high-value flows over dedicated, high-quality paths, MPLS-TE prevents congestion, reduces packet drops, and maintains steady end-to-end performance, even during peak usage times. For NOC engineers working around the clock, this means fewer latency-related issues, shorter troubleshooting cycles, and quicker rerouting during failures. Ultimately, MPLS-TE provides low-latency, stable, and reliable connectivity that enhances user experience, ensures business continuity, improves operational efficiency, and maintains consistent performance for mission-critical applications.

                                 

What is Segment routing? How it is helpful for critical latency driven application?

Segment routing enables the network to choose the path that is the fastest and least loaded, hence minimizing latency and congestion. Traditional shortest-path routing often uses congested paths. By encoding a list of segments (SIDs) directly into the packet, SR lets traffic avoid slow or overloaded links and proceed on a clear low-latency path. An SDN controller and real-time monitoring enable SR-TE to continually check for link utilization, latency, and congestion. In turn, it dynamically directs traffic through the best paths while balancing load across the network. SR also minimizes the heavy signaling and tunnel overhead associated with RSVP-TE, leading to faster responses and simpler operations that further help improve end-to-end performance. A blend of direct path control, dynamic optimization, and simplified structure ensures that the connectivity will be stable, predictable, and of low latency for these critical applications.

SR is a new source routing paradigm that makes the traffic engineering easy because it includes every link in its path of packet in surprising way inside the packet header. Rather than leveraging traditional per-hop signaling protocols such as MPLS-TE, SR uses a stack of segments - predefined instruction like visit specific node, take specific link or applying a specific service. These are expressed as SIDs, and the routers along the path use them step by step. SR provides scalability because no complicated state information needs to be stored in every router and flexibility to select the path. This is indeed helpful in the SDN environment, in which SR can re-arrange traffic dynamically, optimize traffic flows, reduce congestion, and latency. 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.