How to Implement Fog Computing in ns2

To implement the fog computing in Network Simulator 2 (ns2) encompasses to simulate a dispersed computing scenario in which the data processing and records are moved nearer to the data sources, usually at the network’s edge or in fog nodes. This computing expands cloud computing abilities to the edge of the network, allowing quick processing and decreased latency.

In the following below, we provided the necessary steps for the implementation of fog computing in ns2:

Step-by-Step Implementation:

  1. Set Up the NS2 Environment:
  • Make certain that you have NS2 installed on your system.
  • Know more about TCL scripting, as NS2 simulations are controlled via TCL.
  1. Define the Network Topology:
  • Generate a network topology with IoT devices (clients), fog nodes (edge devices), and a central cloud server.

# Define the simulator

set ns [new Simulator]

# Create a trace file for analysis

set tracefile [open out.tr w]

$ns trace-all $tracefile

# Create a NAM file for animation

set namfile [open out.nam w]

$ns namtrace-all $namfile

# Define nodes: IoT devices, fog nodes, and a central cloud server

set iot1 [$ns node]

set iot2 [$ns node]

set iot3 [$ns node]

set fog1 [$ns node]  ;# Fog node 1

set fog2 [$ns node]  ;# Fog node 2

set cloud [$ns node]  ;# Central cloud server

  1. Set Up Links Between Nodes:
  • Configure links amongst the IoT devices, fog nodes, and the cloud server, simulating various network conditions (like changing bandwidths and delays).

# Create duplex links between IoT devices and fog nodes

$ns duplex-link $iot1 $fog1 5Mb 10ms DropTail

$ns duplex-link $iot2 $fog1 5Mb 15ms DropTail

$ns duplex-link $iot3 $fog2 5Mb 20ms DropTail

# Create duplex links between fog nodes and the cloud server

$ns duplex-link $fog1 $cloud 10Mb 50ms DropTail

$ns duplex-link $fog2 $cloud 10Mb 60ms DropTail

  1. Simulate Fog Computing Offloading:
  • Include TCP/UDP agents to the IoT devices and fog nodes to simulate the unloading of computation tasks. You can simulate estimation by transmitting data from IoT devices to fog nodes.

# IoT device 1 sends data to fog node 1 for processing

set tcp_iot1 [new Agent/TCP]

$ns attach-agent $iot1 $tcp_iot1

set tcp_fog1 [new Agent/TCPSink]

$ns attach-agent $fog1 $tcp_fog1

$ns connect $tcp_iot1 $tcp_fog1

# IoT device 2 sends data to fog node 1

set tcp_iot2 [new Agent/TCP]

$ns attach-agent $iot2 $tcp_iot2

set tcp_fog1_2 [new Agent/TCPSink]

$ns attach-agent $fog1 $tcp_fog1_2

$ns connect $tcp_iot2 $tcp_fog1_2

# IoT device 3 sends data to fog node 2

set tcp_iot3 [new Agent/TCP]

$ns attach-agent $iot3 $tcp_iot3

set tcp_fog2 [new Agent/TCPSink]

$ns attach-agent $fog2 $tcp_fog2

$ns connect $tcp_iot3 $tcp_fog2

  1. Simulate Task Processing at the Fog Nodes:
  • To indicate processing at the fog nodes, you can present a delay to imitate computation time before data is deliver to the cloud or back to the IoT devices.

# Simulate processing time at fog node 1 before sending data to the cloud

proc fog_processing {src dst data_processing_time} {

global ns

$ns at $data_processing_time “$src send_data_to $dst”

}

# Example of sending processed data from fog node 1 to the cloud after 1 second

$ns at 1.0 “fog_processing $fog1 $cloud 1.0”

$ns at 1.5 “fog_processing $fog2 $cloud 1.5”

  1. Run the Simulation:
  • State when the simulation should finish and run it. The finish procedure will close the trace files and present NAM for visualization.

# Define the finish procedure

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam out.nam &

exit 0

}

# Schedule the finish procedure at 10 seconds

$ns at 10.0 “finish”

# Run the simulation

$ns run

  1. Analyze the Results:
  • Analyze the packet delivery, delays, throughput and other performance metrics by using the trace file (out.tr).
  • Visualize the network performance and traffic flow by opening the NAM file (out.nam).
  1. Customize and Extend:
  • You can tailor the simulation by:
    • Replicate the various workloads by changing the processing time at the fog nodes.
    • Modifying the network conditions (such as bandwidth, delays) to monitor how fog computing impacts performance.
    • Executing more extreme logic for task offloading, where decisions are based on factors like network conditions, fog node load, or the type of task.
    • Replicating environment situations in which particular tasks are unloaded to the central cloud whether fog nodes are overwhelmed.

Example Summary:

We developed a simple fog computing simulation using ns2 in this instance. IoT devices send data to closer fog nodes for processing, which then may direct data to a central cloud server. The simulation utilizes TCP for consistent data transfer, and the processing delay indicates the estimation at the fog nodes.

Advanced Considerations:

  • For more difficult fog computing environments, consider executing dynamic load balancing, where tasks are unloaded to the least loaded or closest fog node.
  • You can also replicate energy utilization, resource distribution, and mobility of IoT devices, which are vital features of fog computing.

Debugging and Optimization:

  • Utilize the trace-all command to debug the simulation and evaluate packet flows.
  • Modify TCP parameters, refining the unloading logic and fine-tuning the network characteristics to enhance the simulation.

In conclusion, we presented the step-by-step demonstration on how to implement the Fog computing using ns2 simulation tool. We can customize it as per the requirements and expand it for future enhancements. We use this computing to send data directly to the central cloud server. We provide customized implementation of Fog Computing in ns2, designed specifically for your needs. Our team offers top-notch project guidance to ensure your success. We focus on the network’s edge or fog nodes, delivering personalized support for your projects.