Projects On Cloud Computing for CSE Students

Projects On Cloud Computing for CSE Students that you can build for your reasech are listed below, we also work on customised topic moreover provide with topic help as per your area, call us now for experts help. We’ll help you navigate the process, providing tailored topics and guidance to enhance your technical skills. Cloud computing is an intriguing domain that provides enormous scopes to carry out research and develop innovative projects. By involving different factors of this domain such as cloud-related applications, resource handling, and security, we suggest some major projects, which span from beginner to higher levels:

1. Cloud-Based File Storage System

• Outline: To enable users to upload, distribute, and download files, a cloud storage framework has to be created. Various characteristics such as version control, file encryption, and user authentication must be applied.
• Major Mechanisms: MongoDB, Node.js, Google Cloud Storage, and AWS S3.
• Acquired Expertise: User authentication, encryption, file management, and cloud storage.

2. Cloud-Based Chat Application

• Outline: An actual-time chat application should be developed, which assures credibility and scalability by utilizing cloud infrastructure. It is significant to deploy different characteristics such as group chats, message history, and user authentication.
• Major Mechanisms: Node.js, React, Google Cloud Functions, and Firebase.
• Acquired Expertise: Frontend-backend integration, cloud functions, and actual-time communication.

3. Distributed Computing Using Hadoop

• Outline: A Hadoop cluster must be configured. To process extensive datasets, a distributed computing project has to be carried out, like a word count program.
• Major Mechanisms: Java, MapReduce, HDFS, and Apache Hadoop.
• Acquired Expertise: Cluster configuration, big data processing, and distributed computing.

4. Serverless Web Application

• Outline: By means of AWS Lambda, a serverless web application must be created. In Lambda functions, focus on applying backend logic. For handling API requests, plan to utilize AWS API Gateway.
• Major Mechanisms: React, DynamoDB, AWS API Gateway, and AWS Lambda.
• Acquired Expertise: API handling, cloud databases, and serverless framework.

5. Cloud-Based Healthcare System

• Outline: For handling patient data, appointments, and healthcare logs, we intend to develop a cloud-related framework. It is important to assure following healthcare principles and data protection.
• Major Mechanisms: Angular, ASP.NET, SQL Database, and Microsoft Azure.
• Acquired Expertise: Web creation, security adherence, and healthcare data handling.

6. Cloud Resource Management

• Outline: On the basis of expected requirements and latest utilization, handle and enhance cloud resources in a dynamic manner by creating an efficient application. Diverse characteristics such as cost estimation and auto-scaling have to be applied.
• Major Mechanisms: Docker, Python, Google Kubernetes Engine, and AWS EC2.
• Acquired Expertise: Container arrangement, auto-scaling, and resource handling.

7. IoT Data Processing in the Cloud

• Outline: From IoT devices, data must be gathered, processed, and visualized. For that, build an IoT data pipeline with cloud services. Plan to carry out data processing and analytics in actual-time.
• Major Mechanisms: Tableau, AWS Lambda, AWS Kinesis, and AWS IoT Core.
• Acquired Expertise: Data visualization, actual-time analytics, and IoT data processing.

8. Cloud-Based DevOps Pipeline

• Outline: For an example application, the development, testing, and placement operations have to be automated by setting up a CI/CD pipeline in the cloud.
• Major Mechanisms: AWS CodePipeline, Kubernetes, Docker, and Jenkins.
• Acquired Expertise: Continuous integration, continuous deployment, and DevOps approaches.

9. Blockchain on Cloud

• Outline: A blockchain application has to be created (for instance: a basic cryptocurrency). To manage the network and data storage, implement this application with cloud services.
• Major Mechanisms: Solidity, AWS Blockchain, Smart Contracts, and Ethereum.
• Acquired Expertise: Cloud placement, smart contracts, and blockchain creation.

10. Energy-Efficient Cloud Computing

• Outline: With the aid of different optimization methods, the energy usage of cloud data centers has to be examined and minimized. For that, we plan to execute a project.
• Major Mechanisms: Energy-effective algorithms, Java, and CloudSim.
• Acquired Expertise: Green computing, cloud simulation, and energy optimization.

11. Multi-Cloud Deployment and Management

• Outline: Among several cloud providers, implement and handle applications by creating a robust framework. It is crucial to assure fault tolerance and high availability.
• Major Mechanisms: Google Cloud Platform, Azure, AWS, Ansible, and Terraform.
• Acquired Expertise: Automation, infrastructure as code, and multi-cloud management.

12. Cloud-Based Machine Learning Platform

• Outline: To enable users to train and implement machine learning models, an efficient platform should be developed with cloud resources. Focus on applying various characteristics such as automated scaling and model versioning.
• Major Mechanisms: Docker, Flask, TensorFlow, and AWS SageMaker.
• Acquired Expertise: API creation, cloud-related ML services, and machine learning.

13. Secure Cloud Storage with Blockchain

• Outline: In order to assure data morality and protection, the blockchain mechanism has to be integrated with cloud storage.  To allow users to store and obtain files in a safer manner, a framework must be deployed.
• Major Mechanisms: MongoDB, Node.js, AWS S3, and Hyperledger.
• Acquired Expertise: Distributed frameworks, secure storage, and blockchain incorporation.

14. Real-Time Data Analytics Platform

• Outline: For actual-time data analytics, we aim to create a platform with cloud services. Various aspects such as data ingestion, visualization elements, and processing have to be applied.
• Major Mechanisms: Grafana, Apache Spark, AWS Kinesis, and Apache Kafka.
• Acquired Expertise: Cloud services, data analytics, and actual-time data processing.

15. Disaster Recovery Solution in the Cloud

• Outline: Specifically for a cloud-related application, a disaster recovery strategy should be created and applied. It is significant to assure failover abilities, data backup, and replication.
• Major Mechanisms: CloudFormation, AWS RDS, S3, and AWS Backup.
• Acquired Expertise: Cloud infrastructure, data replication, and disaster recovery.

What is the datacenter in CloudSim?

In a cloud platform, a physical data center is indicated by a Datacenter in CloudSim. A collection of computing hosts (physical machines) is included in the Datacenter. For running virtual machines (VMs), these hosts offer computational resources. Simulation of diverse factors of cloud computing can be facilitated by a Datacenter in CloudSim, which is considered as a major element. Some of the potential factors are energy usage, VM scheduling, and resource allocation.

Major Concepts of Datacenter in CloudSim

1. Hosts:

• Several hosts are generally encompassed in each Datacenter. Specific computing resources like bandwidth, storage, RAM, and CPU are included in a host, which is considered as a physical machine.
• For hosting VMs and handling their lifecycle, the hosts are more liable.

2. Datacenter Features:

• Various factors are specifically encompassed, such as cost per unit of resource (bandwidth, storage, memory, and CPU), time zone, specific economic aspects, operating system (for instance: Linux), and architecture (for instance: x86).
• Across diverse contexts, the cost and activity of the data center can be simulated through the use of these features.

3. Resource Allocation:

• In order to allocate resources to the VMs, a resource allocation strategy is utilized by the Datacenter. Some of the general strategies are time-shared and space-shared allocation.
• Among the VMs, in what way the hosts’ computational power is partitioned can be decided by the allocation strategy.

4. Virtual Machines (VMs):

• The Datacenter is responsible for developing and handling VMs. Specific configuration is presented in each VM. It could encompass bandwidth, storage, amount of RAM, and the number of processing elements (PEs).
• Cloudlets (missions) can be executed by VMs. Particularly, the Datacenter schedules these missions.

5. Cloudlets:

• The missions or workloads are depicted by cloudlets, which can be run by VMs. Different features of cloudlets are usage models for CPU, bandwidth, and RAM, output size, file size, and length (number of instructions).

6. Datacenter Broker:

• Among the Datacenter and cloud users, the broker serves as a mediator. For handling the execution and submitting cloudlet and VM demands to the Datacenter, it is more liable.

Instance of Developing a Datacenter in CloudSim

As a means to develop a Datacenter in CloudSim, we offer a sample code snippet:

import org.cloudbus.cloudsim.*;

import org.cloudbus.cloudsim.core.CloudSim;

import java.util.ArrayList;

import java.util.Calendar;

import java.util.LinkedList;

import java.util.List;

public class CloudSimExample {

public static void main(String[] args) {

// Initialize CloudSim library

int numUsers = 1; // Number of cloud users

Calendar calendar = Calendar.getInstance();

boolean traceFlag = false; // mean trace events

// Initialize CloudSim

CloudSim.init(numUsers, calendar, traceFlag);

// Create a Datacenter

Datacenter datacenter = createDatacenter(“Datacenter_0”);

// Create Datacenter Broker

DatacenterBroker broker = createBroker();

int brokerId = broker.getId();

// Create a list of VMs

List<Vm> vmList = new ArrayList<Vm>();

int vmId = 0;

int mips = 1000;

long size = 10000; // Image size (MB)

int ram = 512; // VM memory (MB)

long bw = 1000;

int pesNumber = 1; // Number of CPUs

String vmm = “Xen”; // VMM name

// Create VM

Vm vm = new Vm(vmId, brokerId, mips, pesNumber, ram, bw, size, vmm, new CloudletSchedulerTimeShared());

vmList.add(vm);

// Submit VM list to the broker

broker.submitVmList(vmList);

// Create a list of Cloudlets

List<Cloudlet> cloudletList = new ArrayList<Cloudlet>();

int cloudletId = 0;

long length = 400000;

long fileSize = 300;

long outputSize = 300;

UtilizationModel utilizationModel = new UtilizationModelFull();

// Create Cloudlet

Cloudlet cloudlet = new Cloudlet(cloudletId, length, pesNumber, fileSize, outputSize, utilizationModel, utilizationModel, utilizationModel);

cloudlet.setUserId(brokerId);

cloudletList.add(cloudlet);

// Submit cloudlet list to the broker

broker.submitCloudletList(cloudletList);

// Start the simulation

CloudSim.startSimulation();

// Stop the simulation

CloudSim.stopSimulation();

// Print results

List<Cloudlet> newList = broker.getCloudletReceivedList();

printCloudletList(newList);

System.out.println(“CloudSim example finished!”);

}

private static Datacenter createDatacenter(String name) {

// Create a list to store one or more machines

List<Host> hostList = new ArrayList<Host>();

// Create a list of Processing Elements (PEs or CPUs)

List<Pe> peList = new ArrayList<Pe>();

int mips = 1000;

peList.add(new Pe(0, new PeProvisionerSimple(mips))); // Need to store PE id and MIPS Rating

// Create Host with its id and list of PEs and add them to the list of machines

int hostId = 0;

int ram = 2048; // Host memory (MB)

long storage = 1000000; // Host storage

int bw = 10000;

Host host = new Host(hostId, new RamProvisionerSimple(ram), new BwProvisionerSimple(bw), storage, peList, new VmSchedulerTimeShared(peList));

hostList.add(host); // Add the host to the list

// Create a DatacenterCharacteristics object that stores the properties of a data center

String arch = “x86”; // System architecture

String os = “Linux”; // Operating system

String vmm = “Xen”; // Virtual Machine Monitor

double timeZone = 10.0; // Time zone this resource is located in

double cost = 3.0; // The cost of using processing in this resource

double costPerMem = 0.05; // The cost of using memory in this resource

double costPerStorage = 0.1; // The cost of using storage in this resource

double costPerBw = 0.1; // The cost of using bandwidth in this resource

LinkedList<Storage> storageList = new LinkedList<Storage>(); // We are not adding SAN devices by now

DatacenterCharacteristics characteristics = new DatacenterCharacteristics(arch, os, vmm, hostList, timeZone, cost, costPerMem, costPerStorage, costPerBw);

// Finally, we need to create a Datacenter object

Datacenter datacenter = null;

try {

datacenter = new Datacenter(name, characteristics, new VmAllocationPolicySimple(hostList), storageList, 0);

} catch (Exception e) {

e.printStackTrace();

}

return datacenter;

}

private static DatacenterBroker createBroker() {

DatacenterBroker broker = null;

try {

broker = new DatacenterBroker(“Broker”);

} catch (Exception e) {

e.printStackTrace();

return null;

}

return broker;

}

private static void printCloudletList(List<Cloudlet> list) {

int size = list.size();

Cloudlet cloudlet;

String indent = ”    “;

System.out.println();

System.out.println(“========== OUTPUT ==========”);

System.out.println(“Cloudlet ID” + indent + “STATUS” + indent +

“Data center ID” + indent + “VM ID” + indent + “Time” + indent + “Start Time” + indent + “Finish Time”);

for (int i = 0; i < size; i++) {

cloudlet = list.get(i);

System.out.print(indent + cloudlet.getCloudletId() + indent + indent);

if (cloudlet.getStatus() == Cloudlet.SUCCESS) {

System.out.print(“SUCCESS”);

System.out.println(indent + indent + cloudlet.getResourceId() + indent + indent + indent + cloudlet.getVmId() +

indent + indent + cloudlet.getActualCPUTime() + indent + indent + cloudlet.getExecStartTime() + indent + indent + cloudlet.getFinishTime());

}

}

}

}

Description of the Code

1. Initialize CloudSim:

• CloudSim.init(numUsers, calendar, traceFlag);
• Including the particular number of users, trace flag, and calendar instance, this code sets the CloudSim library.

2. Build Datacenter:

• Datacenter datacenter = createDatacenter(“Datacenter_0”);
• In order to build a datacenter with a particular setup, it calls the createDatacenter method.

3. Develop Datacenter Broker:

• DatacenterBroker broker = createBroker();
• To handle the cloudlets and VMs, it develops a broker.

4. Develop and Submit VMs:

• It develops and submits a collection of VMs to the broker. Particular setups such as bandwidth, storage, RAM, and MIPS are included in each VM.

5. Develop and Submit Cloudlets:

• This code develops and submits a collection of cloudlets (missions) to the broker. Various features such as usage models, file size, and length are encompassed in each cloudlet.

6. Initiate and Terminate Simulation:

• CloudSim.startSimulation(); and CloudSim.stopSimulation();

Emphasizing the domain of cloud computing, several interesting projects are listed out by us, along with brief outlines, major mechanisms, and acquired expertise. By considering a Datacenter in CloudSim, we specified its major concepts, including an instance of developing a Datacenter.

Thesis On Cloud Computing for CSE Students

Thesis On Cloud Computing for CSE Students is a pivotal step in scholar’s career. Explore our collection of compelling thesis topics for CSE students, and let us help you refine your ideas and navigate the research process with dedicated assistance.

1. Rock-hyrax: An energy efficient job scheduling using cluster of resources in cloud computing environment
2. Priority based job scheduling technique that utilizes gaps to increase the efficiency of job distribution in cloud computing
3. Cyber security attack recognition on cloud computing networks based on graph convolutional neural network and graph age models
4. Hotspot resolution in cloud computing: A Γ-robust knapsack approach for virtual machine migration
5. Cyber resilience and cyber security issues of intelligent cloud computing systems
6. Self-Attention conditional generative adversarial network optimised with crayfish optimization algorithm for improving cyber security in cloud computing
7. Barriers to continuance use of cloud computing: Evidence from two case studies
8. CloudAISim: A toolkit for modelling and simulation of modern applications in AI-driven cloud computing environments
9. Complexities to the deployment of cloud computing for sustainability of small construction projects: Evidence from Pakistan
10. Federated dueling DQN based microgrid energy management strategy in edge-cloud computing environment
11. A novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and
12. Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure
13. Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet
14. Combinatorial metaheuristic methods to optimize the scheduling of scientific workflows in green DVFS-enabled edge-cloud computing
15. High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing

16. Conceptualizing hybrid model for influencing intention to adopt cloud computing in North-Eastern Nigerian academic libraries
17. DLJSF: Data-Locality Aware Job Scheduling IoT tasks in fog-cloud computing environments
18. Optimizing Task Scheduling in Cloud Computing: An Enhanced Shortest Job First Algorithm
19. Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment
20. Aerial computing: Enhancing mobile cloud computing with unmanned aerial vehicles as data bridges—A Markov chain based dependability quantification