GNS3 and Mininet are the network emulators, each containing its own significant advantages. On the basis of characteristics, ease of utilization, and performance, we provide comparison of GNS3 and Mininet. This comparison process will assist you to obtain more information about these emulators:

Goal: The main objective is to contrast the characteristics, easy utilization, and effectiveness of Software-Defined Networking (SDN) deployments in GNS3 and Mininet.

Summary of Tools:

  1. GNS3:
  • For physical and virtual network devices, GNS3 is a network emulator with GUI assistance.
  • It facilitates the combination with SDN controllers such as Floodlight, OpenDaylight, and ONOS.
  1. Mininet:
  • Specifically, for SDN study and advancement the formulated network emulator is Mininet.
  • It facilitates Python API and Command-line interface (CLI) for network arrangement.

Comparison Parameters

  1. Installation and Setup
  2. Ease of Use and Configuration
  3. Controller Integration
  4. Network Topology Design
  5. Performance Metrics

Installation and Setup


  • Generally, GNS3 needs installation of GNS3 GUI and server.
  • For virtual hosts, it assists QEMU, Docker containers, and VirtualBox.

Setup Procedures:

  1. Install GNS3 GUI and Server (Ubuntu Instance):

sudo add-apt-repository ppa:gns3/ppa

sudo apt-get update

sudo apt-get install gns3-gui gns3-server

  1. Install Virtualization Software:
    • Docker: sudo apt-get install
    • VirtualBox: sudo apt-get install virtualbox


  • On Ubuntu, Mininet is simple to install.
  • It executes as a CLI-related application.

Setup Procedures:

  1. Install Mininet (Ubuntu Instance):

sudo apt-get update

sudo apt-get install mininet –y

Ease of Use and Configuration


  • GNS3 is referred to as a GUI-based drag-and-drop network model.
  • By means of actual network devices such as Juniper, Cisco, it supports practical simulation.
  • Typically, SDN controllers contain the capability to be combined as Docker containers.


  • Employing CLI commands or Python scripts, Mininet is determined as a CLI-related network model.
  • For direct communication with the network, it offers a command-line interface.

Controller Integration


  • GNS3 assists combination with Docker containers and virtual machines.
  • Normally employed controllers: ONOS, Floodlight, OpenDaylight, Ryu.
  • Instance Setup:
  • It contains the ability to import a Docker container such as OpenDaylight, Floodlight.
  • To the topology, append the controller.


  • By means of RemoteController class, it facilitates external SDN controllers.
  • Normally utilized controllers: Ryu, ONOS, POX, Floodlight, OpenDaylight.
  • Instance Setup:
  • To a Mininet topology, append a RemoteController.

# Example Mininet Topology with Ryu Controller

from import Mininet

from mininet.node import RemoteController, OVSSwitch

from mininet.cli import CLI

from mininet.log import setLogLevel

def simple_topology():

net = Mininet(controller=RemoteController, switch=OVSSwitch)

# Add a remote controller (Ryu)

net.addController(‘c0′, controller=RemoteController, ip=’’, port=6633)

# Add switches and hosts

s1 = net.addSwitch(‘s1’)

s2 = net.addSwitch(‘s2’)

h1 = net.addHost(‘h1′, ip=’’)

h2 = net.addHost(‘h2′, ip=’’)

h3 = net.addHost(‘h3′, ip=’’)

h4 = net.addHost(‘h4′, ip=’’)

# Add links

net.addLink(h1, s1)

net.addLink(h2, s1)

net.addLink(h3, s2)

net.addLink(h4, s2)

net.addLink(s1, s2)

# Start the network




if __name__ == ‘__main__’:



Network Topology Design


  • GNS3 is described as a GUI-related design employing drag-and-drop for hosts, switches, and routers.
  • It permits importing and employing virtual network devices such as Juniper, Cisco.


  • Mininet facilitates CLI commands and Python scripting for network topology structure.
  • By means of Open vSwitch, practical topologies are emulated.

Instance Mininet Commands:

# Linear topology with 3 switches

sudo mn –topo linear,3 –controller remote,ip= –switch ovs –mac –arp

Performance Metrics


  • Through external tools, it facilitates traffic monitoring, iperf, and ping.
  • In virtual links, GNS3 has the capability to simulate packet loss and latency.


  • It supports integrated tools such as tcpdump, iperf, and ping.
  • For autonomous performance testing, Mininet provides a Python API.

Comparative Analysis Table

Parameter GNS3 Mininet
Installation Moderate (Multiple Components) Easy (Single Command)
Ease of Use GUI-Based (Drag-and-Drop) CLI-Based, Python API
Controller Integration Docker, VM Controllers (ODL, ONOS) RemoteController Class (Ryu, POX, ODL)
Network Topology Design GUI (Drag-and-Drop) Python Scripting, CLI Commands
Performance Testing iperf, ping, external tools iperf, ping, tcpdump
Scalability Limited (VM/Docker Resources) Single Host (Open vSwitch Limitations)
Realism High (Real Network Devices) Emulated Network Devices
Use Cases Network Training, Device Testing SDN Prototyping, Research


  • GNS3: Typically, GNS3 is suitable for GUI-based network design, network training, and actual device testing.
  • Mininet: For SDN modelling, study, and performance testing, Mininet is determined as perfect and appropriate.

Next Steps

  • On the basis of certain necessities like SDN research vs. network training, select the tool.
  • Through the utilization of the selected tool, model and deploy a basic SDN project.
  • Aim to assess and contrast performance parameters like packet loss, latency, and throughput.

What are some interesting project ideas from the SDN and NFV domains?

There are numerous project ideas in the Software Defined Networking (SDN) and Network Functions Virtualization (NFV). But some are determined as fascinating and efficient. We offer the most prominent and intriguing project plans from the SDN and NFV fields:

  1. QoS-Aware Traffic Management with SDN and NFV

Goal: Utilizing SDN for traffic categorization and NFV for service chaining, aim to deploy a Quality of Service (QoS) model.


  • Traffic Classification: In an SDN controller such as Ryu, utilize a traffic classifier module.
  • Service Chaining: In order to chain network operations such as NAT, firewalls, and load balancers, it is better to employ NFV.
  • Simulation Setup:
  • For SDN network topology, make use of Mininet.
  • Specifically, for NFV arrangements, aim to employ Open Source MANO (OSM) or OpenStack.
  • Utilizing Docker containers for custom network services like load balancers, firewalls.

Evaluation Metrics: Jitter, Service Setup Time, Latency, Packet Loss.

  1. Anomaly Detection and Mitigation using SDN and NFV

Goal: The main objective of this project is to construct an anomaly identification and reduction framework employing NFV for implementing safety operations and SDN for traffic tracking.


  • Anomaly Detection: An SDN application has to be developed to identify traffic abnormalities through the utilization of flow statistics.
  • Mitigation Strategies: To dynamically implement safety operations such as DDoS protection, IDS/IPS, focus on employing NFV.
  • Simulation Setup:
  • Specifically, for SDN topology, employ Mininet.
  • Through Docker containers, IDS/IPS modules can be deployed.
  • Utilizing Python for custom flow tracking.

Evaluation Metrics: Mitigation Time, Detection Rate, False Positives.

  1. Dynamic Network Slicing with SDN and NFV in 5G Networks

Goal:  Employing SDN controllers and NFV orchestrators, model and deploy dynamic network slicing.


  • SDN Controller Integration: To regulate the network slices, utilize an SDN-related orchestrator.
  • NFV Orchestration: Typically, NFV has to be employed in order to allot virtual network functions (VNFs) dynamically to every slice.
  • Simulation Setup:
  • For SDN-related network slicing, make use of Ryu/ONOS and Mininet.
  • Mainly, for VNF arrangement, aim to employ OSM or OpenStack.
  • Employing Python for custom resource allocation methods.

Evaluation Metrics: Bandwidth Allocation, Resource Utilization, Latency.

  1. Intent-Based Networking for SDN and NFV Management

Goal: To computerize network management for SDN and NFV, focus on utilizing an Intent-Based Networking (IBN) framework.


  • Intent Definition and Translation: For extensive network aims, construct an intent-based API.
  • Policy Management: In order to implement goals, it is appreciable to develop an SDN/NFV strategy engine.
  • Simulation Setup:
  • For a SDN controller, employ ONOS or OpenDaylight.
  • Typically, for NFV arrangement, OSM or OpenStack has to be used.
  • Utilizing Python REST APIs for intent engine.

Evaluation Metrics: Latency, Intent Compliance Rate, Enforcement Time.

  1. SDN and NFV-Based Multi-Cloud Network Management

Goal: To extend numerous cloud platforms employing SDN and NFV, model and deploy a network management framework.


  • Cross-Cloud Connectivity: To determine connectivity among various cloud areas, it is beneficial to employ SDN.
  • Service Deployment: Employing NFV orchestrators, implement VNFs dynamically over clouds.
  • Simulation Setup:
  • For multi-cloud SDN topology, use Open vSwitch and Mininet.
  • Utilizing Python for custom arrangement application.
  • For VNF implementation, aim to employ OpenStack or Kubernetes.

Evaluation Metrics: Throughput, Service Deployment Time, Cross-Cloud Latency.

  1. Security-Oriented Network Function Virtualization (NFV)

Goal: To secure SDN networks from different assaults, deploy a security system utilizing NFV.


  • Security VNFs: It is beneficial to utilize IDS/IPS, firewalls, and DDoS protection operations as VNFs.
  • Traffic Steering: To steer congestion by the security VNFs, employ SDN.
  • Simulation Setup:
  • For SDN topology, Mininet and Ryu/floodlight have to be utilized.
  • Through Docker containers, security VNFs can be deployed.
  • For VNF arrangement, make use of OSM or OpenStack.

Evaluation Metrics: False Positives, Attack Detection Rate, Mitigation Time.

  1. SDN/NFV-Based Network Analytics and Visualization

Goal: For actual-time tracking of SDN/ NFV networks, create an analytics and visualization model.


  • Flow Monitoring: In the SDN controller, utilize a flow tracking module.
  • Network Analytics: For traffic trends and patterns, investigate gathered flow data.
  • Visualization: Specifically, for network visualization, develop a web-related dashboard.
  • Simulation Setup:
  • For SDN topology, utilize POX/ONOS and Mininet.
  • Employing Flask and D3.js for web dashboard.
  • For VNF arrangement, use OpenStack.

Evaluation Metrics: Anomaly Detection Rate, Traffic Volumes, Flow Statistics.

  1. Resource Optimization in SDN and NFV Networks

Goal: In order to stabilize load and decrease delay in SDN/NFV networks, construct a resource optimization system.


  • Load Balancing: To dynamically stabilize load among various network operations, employ SDN.
  • Resource Allocation Algorithms: Focus on creating and utilizing optimization methods.
  • Simulation Setup:
  • Generally, for an SDN controller, ONOS/OpenDayLight and Mininet has to be used.
  • Utilizing C++ or Python for optimization methods.
  • For VNF arrangement, employ OSM or OpenStack.

Evaluation Metrics: Optimization Efficiency, Resource Utilization, Latency.

  1. Edge Computing with SDN and NFV

Goal: For resource enhancement and low-latency applications, model an edge computing infrastructure employing SDN and NFV.


  • Edge Node Deployment: By utilizing Docker containers, implement edge nodes.
  • Traffic Routing: To route traffic dynamically to the closest edge nodes, employ SDN.
  • Simulation Setup:
  • For SDN topology, aim to make use of Ryu and Mininet.
  • By means of Docker containers, edge nodes have to be deployed.
  • For arrangement, utilize Kubernetes or OpenStack.

Evaluation Metrics: Application Response Time, Latency, CPU/Memory utilization.

  1. Machine Learning-Based Traffic Engineering in SDN/NFV Networks

Goal: To enhance traffic engineering in SDN/NFV networks, implement machine learning.


  • Traffic Classification: Generally, to categorize traffic trends, utilize machine learning frameworks.
  • Routing Optimization: On the basis of categorization outcomes, improve traffic routing.
  • Simulation Setup:
  • Mainly, for SDN topology, use POX/Floodlight and Mininet.
  • For VNF arrangement, aim to utilize OpenStack.
  • Employing Python libraries such as TensorFlow, Scikit-learn for machine learning frameworks.

Evaluation Metrics: Routing Efficiency, Classification Accuracy, Latency.

SDN GNS3 Thesis Topics


Below are some project ideas for SDN network design made with GNS3. At, our team of well-trained experts is dedicated to providing the best guidance on all concepts of SDN GNS3. Our services extend to thesis writing and publication, making us your one-stop solution for all your research issues.

  1. Optimized big data management across multi-cloud data centers: Software-defined-network-based analysis
  2. Current trends of topology discovery in OpenFlow-based software defined networks
  3. Multi criteria analysis of controller placement problem in software defined networks
  4. Characterization of IP-Based communication for smart grid using software-defined networking
  5. Queueing analysis of software defined network with realistic openflow–based switch model
  6. An efficient forensics architecture in software-defined networking-IoT using blockchain technology
  7. Cost optimization of secure routing with untrusted devices in software defined networking
  8. QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks
  9. [PDF] Intrusion detection system in software defined networks using machine learning approach
  10. A particle swarm optimization algorithm for controller placement problem in software defined network
  11. Enabling future internet architecture research and experimentation by using software defined networking
  12. Realizing the quality of service (QoS) in software-defined networking (SDN) based cloud infrastructure
  13. Filling the gap between software defined networking and wireless mesh networks
  14. Using GENI for experimental evaluation of software defined networking in smart grids
  15. Optimal placement of virtual network functions in software defined networks: A survey
  16. SNORT based early DDoS detection system using Opendaylight and open networking operating system in software defined networking
  17. Software defined networking: Redefining the future of internet in iot and cloud era
  18. Performance of Botnet Detection by Neural Networks in Software-Defined Networks
  19. Toward highly available and scalable software defined networks for service providers
  20. Effective software‐defined networking controller scheduling method to mitigate DDoS attacks