MININET SIMULATOR

To interpret the performance and activities of the simulated network, evaluate the data as a next stage, once after executing the network experiments in Mininet. Our expertise covers all aspects of MININET Simulator Projects, ensuring top-notch simulation results. Consider the following step-by-step procedure to perform the data analysis in Mininet:

Performance Metrics to Evaluate

  1. Latency: Response time is estimated by using ping.
  2. Throughput: iperf is deployed to evaluate the throughput rate.
  3. Packet Loss: From iperf or ping outcomes, packet loss could be exhibited.
  4. Flow Setup Time: Through the SDN controller, the time interval for configuring flows is determined.
  5. Switch CPU Usage and Memory: It represents the resource allocation of Open vSwitch.

Gradual Procedure of Data Analysis in Mininet

Step 1: Design Network Topology and  Execute  Tests

  1. In Mininet, develop a SDN topology and then begin your network.
  2. Execute the command throughput by iperf and latency by ping and other assessments.
  • Sample Topology Script

# simple_topology.py

From mininet.net import Mininet

From mininet.node import RemoteController

From mininet.cli import CLI

From mininet.log import setLogLevel

Def simple topology ():

Net = Mininet (controller=RemoteController)

# add controller

net.addController (‘c0′, controller=RemoteController, ip=’127.0.0.1’, port=6653)

# add switches

s1 = net.addSwitch (‘s1’)

s2 = net.addSwitch (‘s2’)

s3 = net.addSwitch (‘s3’)

# add hosts

h1 = net.addHost (‘h1′, ip=’10.0.0.1’)

h2 = net.addHost (‘h2′, ip=’10.0.0.2’)

h3 = net.addHost (‘h3′, ip=’10.0.0.3’)

h4 = net.addHost (‘h4′, ip=’10.0.0.4’)

# add links

net.addLink (s1, s2)

net.addLink (s2, s3)

net.addLink (h1, s1)

net.addLink (h2, s1)

net.addLink (h3, s3)

net.addLink (h4, s3)

net.start ()

CLI (net)

Net. Stop ()

If __name__ == ‘__main__’:

SetLogLevel (‘info’)

Simple topology ()

  • Execute the Topology

Sudo python3 simple_topology.py

  • Begin an SDN Controller (Instance: Ryu)

Ryu-manager ryu.app.simple_switch_13

Step 2: Carry out Performance Assessments

  • Latency Test by ping

# In Mininet CLI

H1 ping -c 10 h2

  • Throughput Test by iperf

# Start `iperf` server on h1

H1 iperf -s

# Start `iperf` client on h2

H2 iperf -c h1

  • Flow Setup Time (Packet-In/Out)

For flow setup messages, examine the controller logs.

  • Packet Capture with tcpdump

# Start capturing packets on a host

H1 tcpdump -i h1-eth0 -w h1_capture.pcap

Step 3: Evaluate the Data

Sample Python Script for Data Analysis

  • Install the needed Libraries

pip install matplotlib pandas scapy

  • Assess Latency and Throughput

# data_analysis.py

Import matplotlib.pyplot as plt

Import pandas as pd

# Example latency data from `ping` results

latency_data = {‘Host’: [‘h1-h2’, ‘h1-h3’, ‘h2-h4’],

‘Min RTT (ms)’: [0.25, 0.35, 0.4],

‘Avg RTT (ms)’: [0.3, 0.4, 0.45],

‘Max RTT (ms)’: [0.4, 0.5, and 0.6]}

# Example throughput data from `iperf` results

throughput_data = {‘Host Pair’: [‘h1-h2’, ‘h1-h3’, ‘h2-h4’],

‘Throughput (Mbps)’: [950, 900, and 875]}

# create dataframes

latency_df = pd.DataFrame (latency_data)

throughput_df = pd.DataFrame (throughput_data)

# Plot latency results

Fig, ax = plt.subplots ()

latency_df.plot(x=’Host’, kind=’bar’, ax=ax)

ax.set_ylabel (‘RTT (ms)’)

Plt. Title (‘Latency Analysis’)

plt.show ()

# Plot throughput results

Fig, ax = plt.subplots ()

throughput_df.plot(x=’Host Pair’, y=’Throughput (Mbps)’, kind=’bar’, ax=ax)

Plt. Title (‘Throughput Analysis’)

plt.show ()

  • Evaluate Packet Captures Using Scapy

# packet_analysis.py

From scapy.all import rdpcap, TCP

# Load packet capture file

Packets = rdpcap (‘h1_capture.pcap’)

# analyze TCP flows

tcp_flows = []

For pkt in packets:

If TCP in pkt:

tcp_flows.append ((pkt [IP].src, pkt [IP].dst, pkt [TCP].sport, pkt [TCP].dport))

# Print TCP flows

For flow in tcp_flows:

Print (f’Src: {flow [0]}, Dst: {flow [1]}, Sport: {flow[2]}, Dport: {flow[3]}’)

Step 4: Explanation and Documentation

Report Overview

  1. Introduction: The main objective of experiments and gathered metrics should be described in the introduction part.
  2. Network Topology: Incorporate specific definitions or diagrams.
  3. Findings and Analysis:
  • Latency: Among various hosts, contrast the RTTs.
  • Throughput: Between the host pairs, examine the attained bandwidth.
  • Packet Loss: From ping and iperf outputs, estimate the packet loss.
  • Flow Setup Time: Time duration of flow setup needs to be contrasted from controller logs.
  1. Conclusion: Outline your results and recommend areas for future research and developments.

Software defined networking: what’s the difference between the OpenNet and the Mininet simulators?

OpenNet and Mininet are the very significant simulators which dynamically engage in the simulation process of SDN (Software-Defined Networking) area. Here, we categorize these simulators by specifying with its overview, characteristics, advantages and constraints to acquire knowledge about its each feature:

  1. Mininet
  • Outline:
  • To develop a practical virtual network on a single machine, Mininet is a specifically designed network emulator.
  • It designs controllers, hosts and virtual switches to emulate SDN networks.
  • For the purpose of rapid prototyping and examining SDN applications, this simulator is broadly applicable.
  • Characteristics:
  • SDN Controller Support:
  • Mininet has the capacity to work with significant SDN controllers such as ONOS, POX, Ryu and OpenDaylight.
  • It might synthesize custom or unique controllers.
  • Network Emulation:
  • Network topologies are efficiently simulated with virtual Open vSwitches.
  • Diverse topologies such as tree, custom or linear are dynamically assisted here.
  • Custom Network Topologies:
  • By using Python scripts, it develops custom topologies.
  • Packet loss, latency and bandwidth are critically emulated.
  • CLI and Python API:
  • For experiential control, it includes CLI (Command-Line Interface).
  • This simulator incorporates the Python API for autonomous and examination of replicability.
  • Performance Testing:
  • The tools such as ping and iperf are effectively synthesized.
  • It assists traffic generation and network monitoring.
  • Applicable Areas:
  • To verify SDN applications and prototyping, Mininet is highly adaptable.
  • Particularly for network research and performance assessment, this simulator could be beneficial.
  • Installation:

# Ubuntu Installation

Sudo apt-get install mininet –y

Example of Topology:

# simple_topology.py

From mininet.net import Mininet

From mininet.node import OVSController

From mininet.cli import CLI

From mininet.log import setLogLevel

Def simple topology ():

Net = Mininet (controller=OVSController)

c0 = net.addController (‘c0’)

# add switches

s1 = net.addSwitch (‘s1’)

s2 = net.addSwitch (‘s2’)

# add hosts

h1 = net.addHost (‘h1′, ip=’10.0.0.1’)

h2 = net.addHost (‘h2′, ip=’10.0.0.2’)

h3 = net.addHost (‘h3′, ip=’10.0.0.3’)

# add links

net.addLink (s1, s2)

net.addLink (h1, s1)

net.addLink (h2, s1)

net.addLink (h3, s2)

net.start ()

CLI (net)

Net. Stop ()

If __name__ == ‘__main__’:

SetLogLevel (‘info’)

Simple topology ()

  • Merits:
  • It is a lightweight simulator and effortless for configuration.
  • Practical emulation and virtual switches are encompassed.
  • SDN controllers are deployed for broad consistency.
  • Constraints:
  • Because of resource limitations, Mininet simulator is not appropriate for very extensive-scale networks.
  • Confined within a single machine.
  1. OpenNet
  • Outline:
  • For SDN projects, OpenNet is an effectively developed network simulator and also acts as an emulator.
  • It is primarily tailored for extensive-scale network simulation processes.
  • Characteristics:
  • Scalability:
  • Large-scale network topologies are extensively supported by OpenNet.
  • This simulator incorporates distributed models for scalable network emulation.
  • SDN Controller Support:
  • OpenNet has sufficient capability to synthesize with diverse SDN controllers such as Ryu and OpenDaylight.
  • Custom Protocol Support:
  • The specifications of custom network protocols are effectively accessed.
  • Network Simulation and Emulation:
  • Packet-level simulation is integrated with practical network emulation through OpenNet.
  • In network modeling, it provides sufficient portability.
  • Traffic Analysis:
  • It enables actual-time packet capture and supervising.
  • Pre-defined visualization tools and traffic analysis are encompassed.
  • Installation:
  • As compared to Mininet, this simulator is not a systematized or widely documented supported tool.
  • Crucially simplify with the accurate documents or repository, if it depicts the particular project.

Comparison Table

Feature Mininet OpenNet
Type Network Emulator Network Simulator and Emulator
Compatibility OpenFlow, NetFlow OpenFlow, Custom Protocols
Scalability Limited to Single Machine Distributed Architecture
SDN Controller POX, Ryu, OpenDaylight, ONOS, etc. OpenDaylight, Ryu
Topology Support Linear, Tree, Custom Linear, Custom
Programming Python Python, C++
Traffic Analysis iperf, ping Traffic Capture and Visualization Tools
Use Cases Prototyping, Testing Large-Scale Network Research
Advantages Lightweight, Easy Setup High Scalability, Protocol Customization
Limitations Not Scalable for Large Networks Potential Learning Curve

Conclusion

  • Mininet: Specifically for micro-scale networks, this simulator is perfect for examining SDN deployments and rapid prototyping.
  • OpenNet: To configure and synthesize with controllers, OpenNet is appropriate for extensive-scale simulation, but it might need further efforts.

MININET Simulator Projects

phdprojects.org offers a range of interesting  projects utilizing the MININET Simulator. Explore our unique ideas and concepts by visiting our website. If you require customized services, we are dedicated to providing the best solutions tailored to your needs. While many PhD consultancy services focus solely on paper writing, phdprojects.org stands out by offering a guarantee for both paper writing and publication in esteemed journals.

  1. The SDN shuffle: Creating a moving-target defense using host-based software-defined networking
  2. Data-driven software defined network attack detection: State-of-the-art and perspectives
  3. Balancing flow table occupancy and link utilization in software-defined networks
  4. Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach
  5. Authentication handover and privacy protection in 5G hetnets using software-defined networking
  6. Achieving near-optimal traffic engineering in hybrid software defined networks
  7. Incident-supporting visual cloud computing utilizing software-defined networking
  8. A loss-free multipathing solution for data center network using software-defined networking approach
  9. Enhancing the internet of things with knowledge-driven software-defined networking technology: Future perspectives
  10. On the placement of controllers for designing a wide area software defined networks
  11. Q-placement: Reinforcement-learning-based service placement in software-defined networks
  12. Vehicular software-defined networking and fog computing: Integration and design principles
  13. Developing a cost-effective OpenFlow testbed for small-scale Software Defined Networking
  14. A novel intelligent approach for detecting DoS flooding attacks in software-defined networks
  15. Towards Bayesian-based trust management for insider attacks in healthcare software-defined networks
  16. Phishlimiter: A phishing detection and mitigation approach using software-defined networking
  17. Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network
  18. Toward an optimal solution against denial of service attacks in software defined networks
  19. Phantomnet: Research infrastructure for mobile networking, cloud computing and software-defined networking
  20. A tensor-based big data model for QoS improvement in software defined networks