How to Implement Network Performance Analysis in OMNeT++

To implement network performance analysis in OMNeT++ has several steps that include configuring the simulation environment in which we can measure numerous performance metrics like throughput, latency, packet loss, and jitter. The process needs significant the network topology, setup the simulation parameters, running the simulation, and evaluated the collected data.  The below is the brief structure to execute the network performance analysis in OMNeT++:

Step-by-Step Implementation:

1. Set up OMNeT++ and INET Framework

• Make sure that OMNeT++ and the INET framework are installed and configured properly.
• Generate a new project in OMNeT++ and has contains the INET framework that delivers modules for networking protocols and performance measurement.

2. Design the Network Topology

• Describe the network topology using a .ned file. This topology should denotes the network environment that want to evaluate and measure all relevant nodes and connections.

Example .ned file:

network PerformanceAnalysisNetwork {

submodules:

router1: Router {

@display(“p=100,100”);

}

router2: Router {

@display(“p=300,100”);

}

host1: StandardHost {

@display(“p=50,200”);

}

host2: StandardHost {

@display(“p=350,200”);

}

connections:

router1.pppg++ <–> Ethernet10G <–> router2.pppg++;

host1.ethg++ <–> Ethernet100M <–> router1.pppg++;

host2.ethg++ <–> Ethernet100M <–> router2.pppg++;

}

This simple network consists of two routers and two hosts, that connected via the Ethernet links.

3. Configure Traffic Generation

• Configure the traffic generation among nodes to emulate real network traffic. we can use numerous kinds of traffic, like UDP, TCP, or application-layer protocols.

Example .ini file configuration for traffic generation:

network = PerformanceAnalysisNetwork

sim-time-limit = 100s

# Host 1 sends UDP traffic to Host 2

*.host1.numApps = 1

*.host1.app[0].typename = “UdpBasicApp”

*.host1.app[0].destAddress = “host2”

*.host1.app[0].destPort = 1234

*.host1.app[0].messageLength = 1000B

*.host1.app[0].sendInterval = exponential(1s)

# Host 2 runs a UDP sink to receive traffic

*.host2.numApps = 1

*.host2.app[0].typename = “UdpSink”

This configuration creates UDP traffic from host1 to host2, with an exponential inter-arrival time to emulate the variable traffic.

4. Set up Performance Measurement

• Enable and configure performance metrics in the simulation. OMNeT++ and INET delivers numerous tools to evaluate network performance, like recording throughput, delay, packet loss, and jitter.

Example of enabling scalar recording for performance metrics:

*.router1.**.scalar-recording = true

*.router2.**.scalar-recording = true

*.host1.**.scalar-recording = true

*.host2.**.scalar-recording = true

# Record packet delays

**.scalar-recording-for = “delay”

This configuration enables the recording of scalar data, like delays, for all nodes in the network.

5. Run the Simulation

• Execute the simulation in OMNeT++ to make and gather data on network performance. The simulation will generate output files containing performance metrics.
• Use OMNeT++’s tools to observe the simulation and make sure that traffic is flowing as expected among the nodes.

6. Analyse the Results

• After the simulation, Measure the performance data. OMNeT++ produce scalar (.sca) and vector (.vec) files that contain detailed performance metrics.
• Use OMNeT++’s IDE or other analysis tools like MATLAB, Python to plot graphs and deduce the data.

Example metrics to analyse:

• Throughput: Evaluate the total amount of data successfully received by the destination over time.
• Latency: To compute the end-to-end delay experienced by packets.
• Packet Loss: Regulate the number of packets lost during transmission.
• Jitter: Measure the variation in packet delay over time.

Example Python script for analyzing throughput:

import pandas as pd

import matplotlib.pyplot as plt

# Load scalar data from OMNeT++ .sca file

data = pd.read_csv(‘results/PerformanceAnalysisNetwork.sca’, delimiter=’\t’)

# Filter for throughput metrics

throughput_data = data[data[‘name’].str.contains(‘throughput’)]

# Plot throughput over time

plt.plot(throughput_data[‘time’], throughput_data[‘value’])

plt.xlabel(‘Time (s)’)

plt.ylabel(‘Throughput (bps)’)

plt.title(‘Network Throughput Over Time’)

plt.show()

7. Optimization and Refinement

• Based on the analysis, refine the network configuration to enhance the performance and it contains to adapting the routing protocols, modifying traffic patterns, or changing network topologies.
• Execute the multiple simulations with numerous configurations to compare performance under numerous scenarios.

8. Extend the Analysis

• Expand the performance analysis by contain the more complex scenarios, like mimicking the network congestion, varying traffic loads, or verifying numerous network protocols like TCP vs. UDP.
• Execute advanced performance metrics like Quality of Service (QoS) analysis, and consider factors such as fairness and resource allocation.

From the demonstration we all get knowledge on how to execute and validate the performance for network performance analysis in the OMNeT++ tool. We deliver the additional details about the network performance analysis in further page. If you require any implementation support you can approach us we are ready to guide you.