How to Start Fog RAN Projects Using NS2

To create a Fog-RAN (Fog Radio Access Network) project using NS2 have been involves their replicating of hybrid network structure which associates the Cloud-RAN (C-RAN) and Edge Computing concepts. In this Fog-RAN structure, computational resources are distributed with the edge of the network, close to the last users and although also relying on the cloud resources for heavy computational tasks. This model decreases the latency and enhances the network performance through offloading tasks to the edge nodes (fog nodes).

Since NS2 does not natively helps for Fog-RAN or Edge Computing, we will want to component for main feature the Fog-RAN architecture manually. Under explain to help you get started with a Fog-RAN simulation project using NS2.

Steps to Start Fog-RAN project Using NS2

Key Concepts of Fog-RAN:

  1. Fog Nodes: These are distributed the edge computing resources which deliver the processing power close the user.
  2. Base Stations (BS): This is wireless permits points which link the users to the network, and they are linked together fog nodes and the cloud.
  3. Cloud Resources: This are centralized resources in which intensive computational tasks can be offloaded.
  4. Backhaul Network: The communication connection among the fog nodes, base stations, and the cloud infrastructure for the Backhaul Network.
  5. Latency and Load Balancing: Fog-RAN goals to decrease the latency through processing data at the edge, balancing the computational load among the fog nodes and the cloud.

Steps to Start a Fog-RAN Project in NS2:

  1. Install NS2 and Prerequisites

Enable which NS2 is installed. If it is not installed, observe the installation procedures for the OS.

For Linux:

sudo apt-get install ns2

Designed for macOS or Windows, we can use the package managers such as Homebrew or Cygwin (for Windows) to install NS2.

After installed, validate the installation through processing the simple NS2 simulation script.

  1. Understand Fog-RAN Architecture

The Fog-RAN architecture involves:

  • Edge computing at base stations, in which some computational tasks are processed in the locally we decrease the latency.
  • A centralized cloud infrastructure which maintain the further complex processing.
  • Data offloading method to balance the computational load among edge and cloud resources.
  • Low latency and high throughput Designed for the real-time applications for sample  VR/AR, IoT.
  1. Model the Fog-RAN Architecture in NS2

We replicate the Fog-RAN in NS2, it express to build a network topology that includes:

  • Fog Nodes: Classify this edge nodes in which local processing is completed for the Fog node.
  • Base Stations (BS): This is permit points to client and linked together for nodes and cloud resources.
  • Cloud Resources: Signify the powerful centralized nodes through maximum computational capabilities.
  • Mobile Users: The devices or users linked to the base stations for the mobile users.
  • Backhaul Network: Characterize the communication connection among the fog nodes, base stations, and cloud structure.

Example: Creating the Network Topology

# Create the simulator

set ns [new Simulator]

 

# Create nodes (fog nodes, base stations, cloud node, and mobile users)

set fogNode1 [$ns node]

set fogNode2 [$ns node]

set bs1 [$ns node]    ;# Base Station 1

set bs2 [$ns node]    ;# Base Station 2

set cloudNode [$ns node]   ;# Cloud node

set user1 [$ns node]   ;# Mobile user 1

set user2 [$ns node]   ;# Mobile user 2

# Define links between nodes (fog nodes, base stations, cloud, and mobile users)

$ns duplex-link $fogNode1 $bs1 10Mb 10ms DropTail

$ns duplex-link $fogNode2 $bs2 10Mb 10ms DropTail

$ns duplex-link $bs1 $cloudNode 100Mb 10ms DropTail

$ns duplex-link $bs2 $cloudNode 100Mb 10ms DropTail

$ns duplex-link $bs1 $user1 10Mb 10ms DropTail

$ns duplex-link $bs2 $user2 10Mb 10ms DropTail

In this example:

  • Fog nodes have contains the some sample such as fogNode1 and fogNode2 they are linked to the Base Stations such as bs1 and bs2.
  • Base Stations are linked together the Cloud nodes like as representing centralized resources and Mobile users.
  1. Model Fog Computing and Task Offloading

In Fog-RAN, some tasks such as video processing, AI inference can be offloaded from the mobile device for the fog nodes or the cloud. We can replicate through increase the task offloading logic for the mobile users.

Example: Task Offloading from User to Fog Node

We can design the task offloading process through generating a function which replicates the offloading of computation from the mobile user to the fog node or cloud.

# Define a procedure to offload task from user to fog node

proc offload-task {user fogNode} {

global ns

# Simulate sending a task to the fog node for processing

set task [new Application/Traffic/CBR]

$task set name “task_from_user”

$task attach-agent $user

$ns at 2.0 “$task start”

$ns at 5.0 “$task stop”

# Simulate fog node processing the task

$ns at 3.0 “$fogNode process-task $task”

}

# Simulate task offloading from user1 to fogNode1

$ns at 1.0 “offload-task $user1 $fogNode1”

In this sample, user1 offloads a task to fogNode1 for processing. We can more improve for this establish a decision logic to determine when tasks should be offloaded to the fog node or cloud node according to the factors such as latency, computational load, or network conditions.

  1. Simulate Task Scheduling and Load Balancing

A Complex characterize of Fog-RAN is the load balancing among the fog nodes and the cloud. We can replicate the load balancing through dynamically permitting tasks to either the fog node or the cloud according to their current load.

Example: Dynamic Task Offloading

proc dynamic-offload {user fogNode cloudNode} {

global ns

# Simulate decision logic based on load (for simplicity, assume load is random)

set fogNodeLoad [expr rand()]

set cloudNodeLoad [expr rand()]

# Offload to fog node if its load is lower than cloud’s

if {$fogNodeLoad < $cloudNodeLoad} {

$ns at 1.0 “$user offload-to-fog $fogNode”

} else {

$ns at 1.0 “$user offload-to-cloud $cloudNode”

}

}

Here, we are replicating a dynamic decision process to offload tasks for the fog node or the cloud node according to their load conditions.

  1. Monitor and Analyze Performance

After the replication is processing, we should track the key performance indicators (KPIs) such as:

  • Task offloading efficiency: How well the tasks are offloaded in the fog nodes or cloud.
  • Latency: Calculate the latency in tasks offloaded for the fog or cloud.
  • Network throughput: Estimate the data throughput among users, fog nodes, base stations, and cloud resources for the network throughput.
  • Load balancing effectiveness: How well fine tune the fog nodes and cloud balance for computational tasks in the balancing effectiveness.

We can use NS2 trace files we gather the performance data and examine by tools like AWK or XGraph.

# Example: Using AWK to process trace files

awk ‘{if ($1 == “r”) print $0}’ fog_ran_simulation.tr | awk ‘{total+=1} END {print total}’

  1. Extend and Optimize the Simulation

After the replication is operates and we can:

  • Simulate mobility: Transfer the mobile users with the network and replicate the handovers among base stations and fog nodes.
  • Implement advanced task scheduling algorithms we improve the task offloading according on their real-time network conditions.
  • Include more fog nodes and cloud resources for scaling the replication of larger topologies.
  1. Use More Advanced Tools (Optional)

This replication wants to maintain the further complex Fog-RAN environment, we might consider using the NS3 by extensions such as Fog Computing or Cloud-RAN, or use replicator such as OMNeT++ or GNS3 which provide improve the help for such advanced networking components.

Conclusion

We initialize the Fog-RAN project using NS2:

  1. Create a network topology through fog nodes for base stations of cloud resources, and mobile users.
  2. Model task offloading Create the mobile users to fog nodes or the cloud.
  3. Implement load balancing among the fog nodes and cloud according to the network conditions.
  4. Monitor performance through examine the parameter metrics like as task offloading efficiency, latency, and network throughput.
  5. Extend the replication through mobility models and advanced scheduling algorithms.

Although NS2 does not provide native support for Fog-RAN or Edge Computing, you can simulate it by extending the basic capabilities of NS2 with custom scripts to model task offloading, fog nodes, and cloud resources.

We had clearly expounded the step-by-step procedures to simulate the Fog Radio Access Network in sequential order that were simulated by using the ns2 tool. More information will be shared regarding this process in the upcoming manual.