How to Start Mobile Cloud Computing Projects Using NS3

To start a Mobile Cloud Computing (MCC) project using NS3, it encompasses to configure a network in which mobile devices relieve of computational tasks to cloud servers across a network. MCC is broadly utilized minimizing the load at mobile devices by using cloud resources, to enhance the energy consumption, and developing processing efficiency. NS3 can be replicated diverse network situations like cellular, WiFi, and heterogeneous networks, to learn the MCC applications.

We mentioned below is a detailed guide to configuring a Mobile Cloud Computing project in NS3.

Steps to Start Mobile Cloud Computing Projects in NS3

  1. Install NS3
  1. Download and Install NS3 if we doesn’t installed:

git clone https://gitlab.com/nsnam/ns-3-dev.git ns-3

cd ns-3

./waf configure –enable-examples –enable-tests

./waf build

  1. Verify Installation by executing a simple instance such as LTE or WiFi to verify NS3 is appropriately installed:

./waf –run=lte-simple

  1. Understand Mobile Cloud Computing Components

We will normally function with following components in an MCC simulation:

  • Mobile Devices (MDs): It denotes the users’ devices that will relieve of tasks to the cloud.
  • Edge Cloud Servers: Replicate the servers within nearer proximity like edge or fog to MDs minimizing latency.
  • Central Cloud Server: A centralized server in which intensive computation can be divested.
  • Network Connections: Set up cellular (LTE/5G), WiFi, or a mixture of both, for MCC to replicate diverse network environments.
  1. Set Up a Basic Network Topology for MCC

Initially, make a simple configuration in which mobile devices associate to an access point (WiFi or LTE eNodeB) and offload data to a cloud server. We can be replicated a cloud server like a node including a high-performance setup in NS3.

Example: Basic LTE Network for Offloading Tasks to a Cloud Server

This example configures an LTE network in which several mobile devices offload computational tasks to a cloud server.

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

#include “ns3/mobility-module.h”

#include “ns3/lte-module.h”

#include “ns3/applications-module.h”

using namespace ns3;

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

CommandLine cmd;

cmd.Parse(argc, argv);

// Create LTE and EPC helpers

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

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

lteHelper->SetEpcHelper(epcHelper);

// Create a Packet Gateway node (PGW)

Ptr<Node> pgw = epcHelper->GetPgwNode();

// Create an LTE eNodeB and mobile devices (MDs)

NodeContainer enbNodes;

enbNodes.Create(1);

NodeContainer mdNodes;

mdNodes.Create(3); // Mobile devices

NodeContainer cloudNode;

cloudNode.Create(1); // Cloud server node

// Set up mobility for eNodeB (stationary) and MDs (can be mobile)

MobilityHelper mobility;

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

mobility.Install(enbNodes);

mobility.Install(cloudNode); // Cloud is stationary

mobility.SetPositionAllocator(“ns3::GridPositionAllocator”,

“MinX”, DoubleValue(0.0),

“MinY”, DoubleValue(0.0),

“DeltaX”, DoubleValue(20.0),

“DeltaY”, DoubleValue(20.0),

“GridWidth”, UintegerValue(3),

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

mobility.SetMobilityModel(“ns3::RandomWalk2dMobilityModel”,

“Bounds”, RectangleValue(Rectangle(-50, 50, -25, 50)));

mobility.Install(mdNodes);

// Install LTE devices on eNodeB and MDs

NetDeviceContainer enbLteDevices = lteHelper->InstallEnbDevice(enbNodes);

NetDeviceContainer ueLteDevices = lteHelper->InstallUeDevice(mdNodes);

// Install Internet stack on MDs and cloud server

InternetStackHelper internet;

internet.Install(mdNodes);

internet.Install(cloudNode);

// Assign IP addresses to MDs

Ipv4InterfaceContainer ueIpIfaces = epcHelper->AssignUeIpv4Address(NetDeviceContainer(ueLteDevices));

// Attach MDs to eNodeB

for (uint32_t i = 0; i < mdNodes.GetN(); i++) {

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

}

// Set up cloud server on a point-to-point link with PGW (Packet Gateway)

PointToPointHelper p2p;

p2p.SetDeviceAttribute(“DataRate”, StringValue(“10Gbps”));

p2p.SetChannelAttribute(“Delay”, StringValue(“2ms”));

NetDeviceContainer cloudDevices;

cloudDevices = p2p.Install(pgw, cloudNode);

Ipv4AddressHelper ipv4;

ipv4.SetBase(“1.0.0.0”, “255.255.255.0”);

Ipv4InterfaceContainer cloudInterfaces = ipv4.Assign(cloudDevices);

// Set up UDP applications on MDs to simulate offloading tasks to the cloud server

uint16_t port = 4000;

UdpEchoServerHelper echoServer(port);

ApplicationContainer serverApp = echoServer.Install(cloudNode.Get(0));

serverApp.Start(Seconds(1.0));

serverApp.Stop(Seconds(10.0));

UdpEchoClientHelper echoClient(cloudInterfaces.GetAddress(1), port);

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

echoClient.SetAttribute(“Interval”, TimeValue(Seconds(1.0)));

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

for (uint32_t i = 0; i < mdNodes.GetN(); ++i) {

ApplicationContainer clientApp = echoClient.Install(mdNodes.Get(i));

clientApp.Start(Seconds(2.0 + i));

clientApp.Stop(Seconds(10.0));

}

Simulator::Run();

Simulator::Destroy();

return 0;

}

  1. Simulate Offloading Traffic

Replicate the task offloading, from MDs to the cloud server making data traffic. Utilize applications such as OnOffApplication or UdpEchoApplication replicating diverse task sizes and offloading rates.

Example:

OnOffHelper onOff(“ns3::UdpSocketFactory”, InetSocketAddress(cloudInterfaces.GetAddress(1), port));

onOff.SetAttribute(“DataRate”, StringValue(“500Kbps”));

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

ApplicationContainer offloadApp = onOff.Install(mdNodes.Get(0));

offloadApp.Start(Seconds(2.0));

offloadApp.Stop(Seconds(10.0));

  1. Implement Mobility for MDs

While MCC applications frequently include mobile users then for MDs configure the mobility models replicating user movement. We can utilize models such as RandomWalk2dMobilityModel or ConstantVelocityMobilityModel.

Example:

mobility.SetMobilityModel(“ns3::RandomWaypointMobilityModel”,

“Speed”, StringValue(“ns3::UniformRandomVariable[Min=5.0|Max=20.0]”),

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

mobility.Install(mdNodes);

  1. Evaluate Network and Offloading Performance

We accumulate the parameters to examine the performance of MCC like:

  • Latency: For data, estimate the round-trip time attaining the cloud server and return.
  • Throughput: Monitor the data rate that attained by the MDs.
  • Energy Consumption: For offloading vs. local processing (can use NS-3’s BasicEnergySource module), we can replicate energy consumption.

Accumulate the performance data to utilize FlowMonitor:

FlowMonitorHelper flowmon;

Ptr<FlowMonitor> monitor = flowmon.InstallAll();

monitor->SerializeToXmlFile(“mcc-flowmon.xml”, true, true);

  1. Simulate Handover (Optional for Cellular Networks)

If utilizing LTE then allow handover since MDs travel. It is helpful particularly for situations in which offloading is uninterrupted, and the MD need to switch among the eNodeBs.

Example:

lteHelper->AddX2Interface(enbNodes);

lteHelper->HandoverRequest(Seconds(5.0), ueLteDevices.Get(0), enbLteDevices.Get(0), enbLteDevices.Get(1)); // Trigger a handover

  1. Analyze and Visualize Results
  • NetAnim: Envision MD movements, task offloading, and network connections utilizing NetAnim.
  • Trace Analysis: Examine the trace files that focus on latency, throughput, and packet drops.
  • Pcap Files: For in-depth analysis, seize packets examining task offloading communications.
  • Graphing Tools: We can plot the parameters such as offloading latency and throughput estimating the performance of MCC system.
  1. Experiment with Advanced MCC Scenarios

When we have a simple MCC configuration then we test more advanced situations:

  • Edge and Cloud Computing: Replicate the multi-layer offloading utilizing both edge servers (closer to MDs) and central cloud servers.
  • Offloading Policies: We can execute diverse offloading strategies like latency-based or energy-efficient offloading.
  • Network Type Comparison: Then, we equate the offloading performance through LTE, WiFi, or a combination like heterogeneous networks.

We had provided innovative methodology that helps to execute the Mobile Cloud Computing projects through NS3 simulation tool, has enabled an in-depth analysis of this project. More advanced details planned for the upcoming manual.

We handle a variety of network environments, including cellular, WiFi, and mixed networks. At phdprojects.org, we are your trusted partner for starting mobile cloud computing projects using the NS3 tool. Just share your details with us, and our support team will provide you with innovative ideas and topics. With over two decades of research experience, you can count on us for top-notch research guidance.