How to Simulate Mobile Cloud Computing Projects Using NS3

To Simulate Mobile Cloud Computing (MCC) projects in NS3 have needs to generate a network in which mobile devices offload computational tasks to cloud servers. The simulation can contain numerous contexts such as offloading decisions, energy consumption, communication delays, and network performance under various configurations. The simple components contain mobile devices (clients), cloud servers (cloudlets or remote clouds), and the communication networks that associate them (Wi-Fi, cellular networks, or a hybrid of both).

Steps to Simulate a Mobile Cloud Computing Project in NS3:

1. Install NS3

Make sure that NS3 is properly installed. We can validate the installation with:

./waf –run hello-simulator

2. Basic Network Setup for Mobile Cloud Computing

In a basic MCC simulation, you will have:

• Mobile Devices (Clients): Replicate user devices such as smartphones or tablets that can offload computational tasks.
• Cloud Server or Cloudlets: Servers that manage the computation-intensive tasks for mobile devices.
• Communication Network: A network (Wi-Fi or cellular) that associates the mobile devices to the cloud server.

Example Scenario:

This simulation will configure a basic topology with mobile devices associated to a cloud server over a Wi-Fi network. We can adjust the setup to contain cellular networks or more advanced scenarios with offloading techniques.

Example Code for Mobile Cloud Computing Simulation:

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/mobility-module.h”

#include “ns3/internet-module.h”

#include “ns3/wifi-module.h”

#include “ns3/applications-module.h”

#include “ns3/point-to-point-module.h”

#include “ns3/log.h”

using namespace ns3;

NS_LOG_COMPONENT_DEFINE (“MobileCloudComputing”);

int main (int argc, char *argv[])

{

CommandLine cmd;

cmd.Parse (argc, argv);

// Create nodes: Mobile devices and Cloud server

NodeContainer mobileNodes;

mobileNodes.Create (2); // Two mobile devices

NodeContainer cloudServerNode;

cloudServerNode.Create (1); // One cloud server

// Setup Wi-Fi network between mobile devices and the cloud server

WifiHelper wifi;

wifi.SetStandard (WIFI_PHY_STANDARD_80211ac);  // Wi-Fi 5 (802.11ac)

YansWifiChannelHelper wifiChannel = YansWifiChannelHelper::Default ();

YansWifiPhyHelper wifiPhy = YansWifiPhyHelper::Default ();

wifiPhy.SetChannel (wifiChannel.Create ());

WifiMacHelper wifiMac;

Ssid ssid = Ssid (“ns3-MCC”);

wifiMac.SetType (“ns3::StaWifiMac”, “Ssid”, SsidValue (ssid), “ActiveProbing”, BooleanValue (false));

// Install Wi-Fi devices on mobile nodes

NetDeviceContainer mobileDevices;

mobileDevices = wifi.Install (wifiPhy, wifiMac, mobileNodes);

// Configure the Access Point (AP) for the Cloud Server

wifiMac.SetType (“ns3::ApWifiMac”, “Ssid”, SsidValue (ssid));

NetDeviceContainer cloudDevice = wifi.Install (wifiPhy, wifiMac, cloudServerNode);

// Set mobility for the cloud server (stationary)

MobilityHelper mobilityCloud;

mobilityCloud.SetMobilityModel (“ns3::ConstantPositionMobilityModel”);

mobilityCloud.Install (cloudServerNode);

// Set mobility for mobile devices

MobilityHelper mobilityMobile;

mobilityMobile.SetPositionAllocator (“ns3::GridPositionAllocator”,

“MinX”, DoubleValue (0.0),

“MinY”, DoubleValue (0.0),

“DeltaX”, DoubleValue (5.0),

“DeltaY”, DoubleValue (5.0),

“GridWidth”, UintegerValue (2),

“LayoutType”, StringValue (“RowFirst”));

mobilityMobile.SetMobilityModel (“ns3::RandomWaypointMobilityModel”,

“Speed”, StringValue (“ns3::UniformRandomVariable[Min=1.0|Max=10.0]”),

“Pause”, StringValue (“ns3::ConstantRandomVariable[Constant=2.0]”));

mobilityMobile.Install (mobileNodes);

// Install Internet Stack (TCP/IP) on all nodes

InternetStackHelper internet;

internet.Install (mobileNodes);

internet.Install (cloudServerNode);

// Assign IP addresses

Ipv4AddressHelper ipv4;

ipv4.SetBase (“10.1.1.0”, “255.255.255.0”);

Ipv4InterfaceContainer mobileIp = ipv4.Assign (mobileDevices);

Ipv4InterfaceContainer cloudIp = ipv4.Assign (cloudDevice);

// Set up offloading simulation using a UDP client-server model

uint16_t cloudPort = 5000;

UdpServerHelper cloudServer (cloudPort);

ApplicationContainer serverApp = cloudServer.Install (cloudServerNode.Get (0));

serverApp.Start (Seconds (1.0));

serverApp.Stop (Seconds (10.0));

UdpClientHelper mobileClient1 (cloudIp.GetAddress (0), cloudPort);

mobileClient1.SetAttribute (“MaxPackets”, UintegerValue (10));

mobileClient1.SetAttribute (“Interval”, TimeValue (Seconds (0.1)));

mobileClient1.SetAttribute (“PacketSize”, UintegerValue (1024));  // Simulate task size

UdpClientHelper mobileClient2 (cloudIp.GetAddress (0), cloudPort);

mobileClient2.SetAttribute (“MaxPackets”, UintegerValue (10));

mobileClient2.SetAttribute (“Interval”, TimeValue (Seconds (0.1)));

mobileClient2.SetAttribute (“PacketSize”, UintegerValue (1024));

ApplicationContainer clientApp1 = mobileClient1.Install (mobileNodes.Get (0));

ApplicationContainer clientApp2 = mobileClient2.Install (mobileNodes.Get (1));

clientApp1.Start (Seconds (2.0));

clientApp1.Stop (Seconds (10.0));

clientApp2.Start (Seconds (3.0));

clientApp2.Stop (Seconds (10.0));

// Enable tracing

wifiPhy.EnablePcap (“mcc-wifi”, cloudDevice.Get (0));

// Run the simulation

Simulator::Run ();

Simulator::Destroy ();

return 0;

}

Explanation of Code:

1. Wi-Fi Setup: The mobile devices are associated to the cloud server across a Wi-Fi network (802.11ac).
2. Mobility Models: The cloud server is stationary, and the mobile devices move using a RandomWaypointMobilityModel.
3. UDP Client-Server: A simple offloading process is emulated using UDP traffic. Each mobile device transfers offload requests to the cloud server (represented as data packets).
4. Packet Size: The PacketSize metric of the UDP client signifies the size of the computation task offloaded to the cloud server.
5. Application: The UdpServerHelper and UdpClientHelper emulate the communication among the mobile device and cloud server.

3. Simulating Cellular Networks for Mobile Cloud Computing

If we need to replicate a cellular network rather than Wi-Fi, we can utilize the LTE module provided in NS3:

#include “ns3/lte-helper.h”

// Replace Wi-Fi network setup with LTE setup

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();

Ptr<PointToPointEpcHelper> epcHelper = CreateObject<PointToPointEpcHelper> ();

lteHelper->SetEpcHelper (epcHelper);

NodeContainer enbNodes;  // LTE eNodeBs (base stations)

enbNodes.Create (1);  // Single eNodeB

// Set mobility for the LTE eNodeB (stationary base station)

MobilityHelper enbMobility;

enbMobility.SetMobilityModel (“ns3::ConstantPositionMobilityModel”);

enbMobility.Install (enbNodes);

// Install LTE devices on mobile nodes and eNodeB

NetDeviceContainer enbLteDevices = lteHelper->InstallEnbDevice (enbNodes);

NetDeviceContainer ueLteDevices = lteHelper->InstallUeDevice (mobileNodes);

// Attach UEs (mobile devices) to eNodeB (base station)

lteHelper->Attach (ueLteDevices, enbLteDevices.Get (0));

This replaces the Wi-Fi setup and associates the mobile devices to the cloud server through LTE.

4. Offloading Decisions and Energy Consumption

We can add decision-making logic for task offloading based on:

• Energy Consumption: Design the energy consumption of offloading by adapting parameters such as transmission power or task size.
• Latency: Evaluate the latency in offloading tasks to the cloud and utilize this to decide whether offloading is valuable.

5. Network and Task Offloading Metrics

Key metrics to measure in mobile cloud computing simulations that contain:

• Energy Consumption: Evaluate the energy used by mobile devices in the course of communication and offloading.
• Task Offloading Latency: assess the time it takes for mobile devices to offload tasks to the cloud and receive responses.
• Throughput: Measure the rate at which mobile devices can offload tasks to the cloud.
• Offloading Success Rate: The percentage of tasks successfully offloaded to the cloud.

6. Simulating More Complex Offloading Scenarios

To emulate more advanced mobile cloud computing scenarios, that can:

• Implement Decision Algorithms: Add logic for deciding whether to offload a task in terms of network conditions, energy levels, or task complexity.
• Hybrid Networks: Replicate both Wi-Fi and cellular networks and permit mobile devices to switch among them in terms of signal strength or cost.
• Edge Computing: Add cloudlets or edge servers to offload tasks locally rather than a remote cloud server to minimize delay.

7. Run the Simulation

Compile and execute simulation with:

./waf –run scratch/<your-mcc-simulation-file>

8. Advanced Features

We can prolong the simulation with more advanced behaviours like:

• Task Splitting: Replicate splitting large computational tasks via multiple cloud servers or devices.
• Network Congestion: Replicate different network conditions, like congestion or packet loss, and measure the effects on offloading decisions.
• Security: Establish encryption or other security mechanisms and investigate their effects on communication delays and offloading performance.

The above following instruction shown on how to simulate and extend the execution for Mobile Cloud computing in ns3 tool and also we provide more significant information regarding the Mobile Cloud Computing.

To explore Mobile Cloud Computing Projects utilizing NS3, you can visit phdprime.com. We are equipped with a comprehensive range of tools and resources to offer you the most suitable research ideas and topics customized to your requirements.