How to Begin Implement Edge Computing Project in NS3

To begin executing Edge Computing projects using NS3, we will want to replicate a network, which utilises edge nodes that are near-client servers for computational tasks, data storage, and low-latency processing. Following is a series of steps to get started with edge computing project using NS3:

Steps to Begin Implementing an Edge Computing Projects in NS3

1. Understand Edge Computing Concepts

• Key Components:
  • Edge Nodes: Servers or devices are near end-users to act for calculation.
  • Clients: Devices such as IoT sensors, smartphones, or computers.
  • Core Network: Connects edge nodes into central data centers or cloud.
• Use Cases:
  • Real-time applications such as autonomous vehicles, AR/VR.
  • Content caching and delivery.
  • IoT data processing.

2. Set Up NS3 Environment

• We should install the new version of NS3 on the system.
• Learn about following modules of NS3:
  • Internet Module: It supports for TCP/IP stack.
  • Applications Module: Designing application-layer communications.
  • PointToPoint or Wi-Fi Module: For wired and wireless connectivity.

3. Define Project Objectives

• Key project’s goals of edge computing such as:
  • Offloading tasks from clients to edge nodes.
  • Latency and throughput analysis.
  • Performance comparison among the edge and cloud processing.

4. Design the Edge Computing Topology

• Clients:
  • It denotes the devices that are creating data or demanding computation.
• Edge Nodes:
  • Locate the nodes among clients and the cloud to locally execute the demands.
• Core Network:
  • For cloud computing fallback, it has central servers or data centers.

5. Basic Example Simulation

Below is an instance of basic edge computing scenario in which clients are offload tasks to an edge server:

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

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

#include “ns3/applications-module.h”

using namespace ns3;

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

CommandLine cmd;

cmd.Parse(argc, argv);

// Create nodes

NodeContainer clients, edgeNodes, cloudNodes;

clients.Create(3);    // 3 client devices

edgeNodes.Create(1);  // 1 edge server

cloudNodes.Create(1); // 1 cloud server

// Set up Point-to-Point links

PointToPointHelper p2p;

p2p.SetDeviceAttribute(“DataRate”, StringValue(“100Mbps”));

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

NetDeviceContainer clientToEdge, edgeToCloud;

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

clientToEdge.Add(p2p.Install(clients.Get(i), edgeNodes.Get(0)));

}

edgeToCloud.Add(p2p.Install(edgeNodes.Get(0), cloudNodes.Get(0)));

// Install Internet stack

InternetStackHelper internet;

internet.Install(clients);

internet.Install(edgeNodes);

internet.Install(cloudNodes);

// Assign IP addresses

Ipv4AddressHelper ipv4;

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

ipv4.Assign(clientToEdge);

ipv4.SetBase(“10.1.2.0”, “255.255.255.0”);

ipv4.Assign(edgeToCloud);

// Simulate edge processing using UDP communication

uint16_t port = 8080;

UdpEchoServerHelper echoServer(port);

ApplicationContainer edgeApp = echoServer.Install(edgeNodes.Get(0));

edgeApp.Start(Seconds(1.0));

edgeApp.Stop(Seconds(10.0));

UdpEchoClientHelper echoClient(Ipv4Address(“10.1.1.1”), port);

echoClient.SetAttribute(“MaxPackets”, UintegerValue(5));

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

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

ApplicationContainer clientApp = echoClient.Install(clients.Get(0));

clientApp.Start(Seconds(2.0));

clientApp.Stop(Seconds(10.0));

Simulator::Run();

Simulator::Destroy();

return 0;

}

6. Enhance the Simulation

• Task Offloading:
  • Replicate the offloading computational responsibilities from clients to edge servers.
  • Evaluate resource consumption at edge and cloud nodes.
• Traffic Patterns:
  • Launch various traffic models such as sensor data, video streaming, or HTTP demands.
• Latency and Failover:
  • Integrate delays for replicating fallback to cloud once edge servers are overburdened.

7. Performance Metrics

• Latency:
  • Estimate the time taken to destination for task completion.
• Throughput:
  • Measure rate of data that are delivered among the clients and edge nodes.
• Energy Efficiency:
  • Examine energy utilization for edge and cloud processing.
• Server Utilization:
  • Observe the resource consumption at edge and cloud servers.

8. Advanced Features

• Content Caching:
  • Mimic caching strategies for quicker content delivery at edge nodes.
• Dynamic Resource Allocation:
  • For edge servers, execute the load balancing or dynamic scaling.
• Edge Collaboration:
  • Replicate numerous edge nodes to collaborate for managing client demands.
• Security:
  • Design secure interaction among the clients and edge/cloud nodes such as encryption.

9. Visualization and Analysis

• Envision network communications to utilise NetAnim.
• Examine performance parameters for in-depth insights to leverage tools like Python or MATLAB.

10. Extensions

• IoT Integration:
  • Mimic IoT devices that are making data for edge processing.
• Mobile Edge Computing (MEC):
  • Integrate mobility patterns for replicating the clients which are shifting through vehicles, drones.
• SDN Integration:
  • For efficient resource and traffic management, we will need to exploit SDN controllers.

Above demonstration will help you to implement the Edge Computing Projects in NS3 by designing topology, improving simulation, and performing computational tasks. We also provided extension of this project. Whenever, you require advanced insights concerning this project, you can acquire them from us.