How to Implement Network Task Offloading in NS2

To implement Network Task Offloading in NS2 has numerous steps that needs to mimic the transfer of computational tasks from one node such as a mobile device to another that is an edge server or cloud server to enhance energy consumption, delay, or computational efficiency. Task offloading is a key feature in mobile edge computing (MEC) or cloud computing scenarios in which limited resource devices offload tasks to more powerful servers. For any types of implementation guidance feel free to contact us we will provide you with best results.

Here’s a step-by-step guide to implementing Network Task Offloading in NS2:

Step-by-Step Guide to Implement Network Task Offloading in NS2

1. Understand the Scenario:

• The objective is of task offloading is to transmission the computational tasks such as data processing, image recognition, or heavy calculations from a resource-limited node such as mobile device to a resource-rich node like an edge server or cloud server.
• We will replicate decision-making for when to offload tasks according to the metrics like available bandwidth, remaining energy, or task size.

2. Set Up the Network Topology:

• Generate a simple network in which some nodes behave as mobile devices (source nodes) and others act as edge servers or cloud servers (destination nodes for offloading).

Example OTcl script for network setup:

set ns [new Simulator]

set nf [open out.tr w]

$ns trace-all $nf

set namfile [open out.nam w]

$ns namtrace-all $namfile

# Define mobile devices and edge/cloud servers

set mobile_node [$ns node]     ;# Mobile device (source of tasks)

set edge_server [$ns node]     ;# Edge server (destination for offloaded tasks)

# Create duplex link between mobile device and edge server

$ns duplex-link $mobile_node $edge_server 10Mb 10ms DropTail

# Run simulation

$ns at 6.0 “finish”

proc finish {} {

global ns nf namfile

$ns flush-trace

close $nf

close $namfile

exec nam out.nam &

exit 0

}

$ns run

• This simple topology associated a mobile node to an edge server through a duplex link.

3. Define Task Offloading Logic:

• The next step is to execute task offloading logic. This logic will decide either a task should be implemented locally on the mobile node or offloaded to the edge server. The decision could rely on numerous factors like available bandwidth, processing power, or energy consumption.

Example decision logic based on energy and task size:

proc decide_offloading {task_size energy_level bandwidth} {

set threshold_energy 100    ;# Threshold for offloading

set threshold_task_size 500 ;# Minimum task size for offloading

set threshold_bandwidth 5   ;# Minimum bandwidth for offloading

if { $energy_level < $threshold_energy || $task_size > $threshold_task_size || $bandwidth > $threshold_bandwidth } {

return “offload”

} else {

return “local”

}

}

# Offload or execute the task locally based on the decision

proc handle_task {task_size energy_level bandwidth} {

set decision [decide_offloading $task_size $energy_level $bandwidth]

if { $decision == “offload” } {

puts “Task is offloaded to the edge server”

offload_task_to_server $task_size

} else {

puts “Task is processed locally on the mobile device”

process_locally $task_size

}

}

• This script establishes a function (decide_offloading) to control either a task should be offloaded according to a task size, energy, and bandwidth. If the task is offloaded, the offload_task_to_server function manages the interaction to the edge server.

4. Simulate Task Offloading and Processing:

• Once the decision to offload is made, we mimic the process of sending the task to the edge server and receiving the outcomes.

Task offloading example:

# Simulate sending the task to the edge server

proc offload_task_to_server {task_size} {

puts “Offloading task of size $task_size to the edge server…”

set udp [new Agent/UDP]

set null [new Agent/Null]

$ns attach-agent $mobile_node $udp

$ns attach-agent $edge_server $null

$ns connect $udp $null

set task [new Application/Traffic/CBR]

$task attach-agent $udp

$task set packetSize_ [expr $task_size * 10]

$task set rate_ 1Mb

# Start offloading task at 2 seconds

$ns at 2.0 “$task start”

$ns at 3.0 “$task stop”

}

# Simulate local task processing

proc process_locally {task_size} {

puts “Processing task of size $task_size locally…”

# Simulate processing time

$ns at 2.0 “puts \”Task of size $task_size processed locally\””

}

• In this sample, if the task is offloaded, the offload_task_to_server function transfers the task data over UDP to the edge server that mimics the transfer of computation. Local processing is mimicked by printing the task size and a processing delay.

5. Simulate Return of Results from Edge Server:

• Once the task is processed on the edge server, to mimic the return of results back to the mobile node.

Example of returning results to the mobile node:

proc return_results_to_mobile {task_size} {

puts “Returning results of task size $task_size from edge server to mobile node…”

set udp_result [new Agent/UDP]

$ns attach-agent $edge_server $udp_result

$ns connect $udp_result $mobile_node

set result_data [new Application/Traffic/CBR]

$result_data attach-agent $udp_result

$result_data set packetSize_ [expr $task_size * 5] ;# Simulate result size

$result_data set rate_ 1Mb

# Send results at 4 seconds

$ns at 4.0 “$result_data start”

$ns at 5.0 “$result_data stop”

}

# Call this function after offloading task to server

$ns at 3.0 “return_results_to_mobile $task_size”

• This replicates to sending the processed result back to the mobile node after it has been estimated on the edge server.

6. Simulate Offloading Decision in Real-Time:

• To enthusiastically mimic the task offloading during the simulation, we can trigger the offloading logic according to real-time conditions such as energy level or bandwidth.

Example of periodic task offloading:

# Periodically trigger offloading decision

proc periodic_offloading {} {

set task_size 600

set energy_level 90

set bandwidth 10

handle_task $task_size $energy_level $bandwidth

# Check again after some time

$ns at 6.0 “periodic_offloading”

}

# Start periodic task offloading

$ns at 1.0 “periodic_offloading”

• This replicates a scenario in which tasks are occasionally generated, and the decision to offload or process locally is made according to the variation of network conditions.

7. Run the Simulation and Analyze Results:

• After configuring the task offloading logic, execute the simulation and inspect the outcomes using the trace file generated by NS2.

ns your_script.tcl

• Measure the trace file to assess how many tasks were offloaded versus processed locally, and inspect network performance metrics such as latency, throughput, or energy consumption.

8. Analyse Task Offloading Metrics:

• Offloading Frequency: Track how usually tasks are offloaded to the server versus processed locally.
• Latency: Assess the time delay among the task initiation on the mobile device and the return of the consequence from the server.
• Energy Consumption: To mimic energy usage on the mobile device and compare the energy saved by offloading tasks versus local processing.

Example AWK script to evaluate task offloading latency:

awk ‘

BEGIN { start_time = 0; end_time = 0; }

{

if ($1 == “s” && $4 == “UDP”) start_time = $2;

if ($1 == “r” && $4 == “UDP”) end_time = $2;

}

END {

latency = end_time – start_time;

print “Task offloading latency:”, latency, “seconds”;

}’ out.tr

• This script helps to assess the latency among sending a task to the edge server and receiving the outcome.

9. Visualize the Offloading Process in NAM:

• Use NAM (Network Animator) to envision the network activity, that has the task offloading process and end result transmission among the mobile node and the edge server.

nam out.nam

• In NAM, we can monitor on how tasks are offloaded and processed in real-time, beside with the flow of information among the mobile node and the server.

Summary:

To execute Network Task Offloading in NS2:

1. Set up a basic network topology connecting mobile devices to edge or cloud servers.
2. Implement decision-making logic to regulate either to offload a task or process it locally, according to the metrics such as energy, bandwidth, or task size.
3. Simulate the task offloading process, in which tasks are transmitted from the mobile node to the server for processing.
4. Simulate the return of results from the server to the mobile node.
5. Analyze network performance that contains latency and offloading frequency, using trace files.
6. Visualize the simulation using NAM to monitor on how the offloading process performs in real-time.

At the end, we thorough the manual and provide the valuable insights regarding how to implement the Network Task Offloading in ns2 tool. Further details regarding the implementation of the Network Task Offloading in diverse simulations will be provided.