IEEE Projects On Cloud Computing

IEEE Projects On Cloud Computing is an efficient and fast-growing research area that offers scholars a wide range of opportunities to conduct research and develop projects. In this page you can explore the latest IEEE project ideas and topics on Cloud Computing. Stay ahead with cutting-edge technologies and get expert guidance to complete your work on time.

By involving different factors of this technology, we provide a few topic suggestions which are more ideal for cloud computing-based IEEE projects:

  1. Dynamic Resource Allocation and Management
  • Outline: To improve energy effectiveness and resource usage, carry out dynamic resource allocation in cloud data centers by creating algorithms.
  • Goal: On the basis of workload requirements, adapt resources in a dynamic manner to minimize operational expenses and enhance functionality.
  • Mechanisms: Azure, AWS, Java, Python, and CloudSim.
  1. Secure Data Storage and Access Control in Multi-Cloud Environments
  • Outline: Among several cloud providers, a safer data storage and access control technique has to be applied.
  • Goal: In multi-cloud implementations, focus on reducing risks as well as assuring data morality, accessibility, and privacy.
  • Mechanisms: Google Cloud, Azure, AWS, Cryptography, and Blockchain.
  1. Energy-Efficient Cloud Computing
  • Outline: For cloud computing platforms, the energy-effective algorithms and frameworks have to be modeled and applied.
  • Goal: Plan to enhance sustainability and minimize the data centers’ carbon footprint.
  • Mechanisms: Python, Java, CloudSim, and GreenCloud.
  1. IoT Integration with Cloud Computing
  • Outline: A platform must be developed, which facilitates actual-time data processing and analytics by combining IoT devices with cloud services.
  • Goal: By utilizing the computational power and scalability of the cloud, we intend to improve IoT abilities.
  • Mechanisms: Python, Node.js, MQTT, Azure IoT Hub, and AWS IoT.
  1. Machine Learning as a Service (MLaaS)
  • Outline: To provide machine learning services to users, an appropriate cloud-related platform should be created.
  • Goal: For different applications, required and scalable machine learning abilities have to be offered.
  • Mechanisms: Python, TensorFlow, Google AI Platform, and AWS SageMaker.
  1. Disaster Recovery and Business Continuity Planning
  • Outline: At the time of failures, business endurance has to be assured. For that, a disaster recovery approach must be applied with cloud services.
  • Goal: Through automating backup and recovery operations, plan to reduce data loss and idle time.
  • Mechanisms: Terraform, Google Cloud Backup, Azure Site Recovery, and AWS Backup.
  1. Edge Computing and Cloud Integration
  • Outline: A robust framework should be created, which supports less-latency applications by combining edge computing with cloud services.
  • Goal: By processing data nearer to the sources, we aim to minimize bandwidth utilization and enhance response times.
  • Mechanisms: Docker, Kubernetes, Azure IoT Edge, and AWS Greengrass.
  1. Blockchain-Based Cloud Security
  • Outline: Specifically in cloud computing, the security and reliability must be improved with the aid of blockchain mechanism.
  • Goal: In cloud platforms, focus on enhancing reliability and protecting data transactions.
  • Mechanisms: Azure, AWS, Solidity, Hyperledger Fabric, and Ethereum.
  1. Cloud-Based Big Data Analytics
  • Outline: For big data processing and analytics, a cloud-related environment has to be created.
  • Goal: In order to process and examine extensive datasets, scalable approaches have to be offered.
  • Mechanisms: Google BigQuery, AWS EMR, Apache Spark, and Apache Hadoop.
  1. Software-Defined Networking (SDN) in Cloud Computing
  • Outline: In cloud platforms, enhance network handling and functionality by modeling and applying SDN solutions.
  • Goal: Concentrate on improving network security, scalability, and adaptability.
  • Mechanisms: Azure, AWS, Mininet, OpenDaylight, and OpenFlow.
  1. Serverless Architecture for Microservices
  • Outline: To implement and handle microservices in the cloud, a serverless framework should be applied.
  • Goal: It is significant to enhance scalability and minimize operational intricacy.
  • Mechanisms: Kubernetes, Docker, Google Cloud Functions, Azure Functions, and AWS Lambda.
  1. Cloud-Based Learning Management System (LMS)
  • Outline: For academic universities, a cloud-related LMS must be developed.
  • Goal: To access from anyplace, we plan to offer adaptable and scalable learning approaches.
  • Mechanisms: Node.js, React, Azure, AWS, Canvas, and Moodle.
  1. Real-Time Data Processing in Cloud
  • Outline: To acquire, process, and examine data in actual-time, a platform has to be created with cloud services.
  • Goal: For decision-making, the perceptions and analytics should be offered in actual-time.
  • Mechanisms: Google Cloud Dataflow, AWS Kinesis, Apache Flink, and Apache Kafka.
  1. Federated Learning in Cloud Computing
  • Outline: Without revealing raw data, the machine learning models have to be trained by several entities in a collaborative manner. For that, apply federated learning models.
  • Goal: In addition to supporting collaborative learning, improve security and confidentiality.
  • Mechanisms: Python, Azure, AWS, PySyft, and TensorFlow Federated.
  1. Cost Optimization Strategies for Cloud Services
  • Outline: In utilizing cloud services, the cost should be improved by creating policies and tools.
  • Goal: Along with preserving functionality, various firms must minimize their cloud expense.
  • Mechanisms: Java, Python, Google Cloud Billing, Azure Cost Management, and AWS Cost Explorer.
  1. AI-Driven Cloud Resource Management
  • Outline: To handle cloud resources in an effective manner, the AI algorithms have to be applied.
  • Goal: Focus on enhancing functionality, minimizing costs, and improving resource allocations.
  • Mechanisms: Python, TensorFlow, Azure Machine Learning, and AWS SageMaker.
  1. Hybrid Cloud Management Platform
  • Outline: As a means to handle hybrid cloud settings in an appropriate manner, a platform must be created.
  • Goal: Public and private clouds have to be effectively handled and combined.
  • Mechanisms: Azure, AWS, OpenStack, and VMware vSphere.
  1. Cloud-Based Video Streaming Service
  • Outline: By means of cloud services, a scalable video streaming environment has to be developed.
  • Goal: With less latency, we intend to offer high-quality video streaming.
  • Mechanisms: Google Cloud Video Intelligence, Azure Media Services, and AWS Media Services.
  1. Healthcare Data Management in Cloud
  • Outline: Particularly for handling and examining healthcare data, a cloud-related framework should be deployed.
  • Goal: For healthcare providers, plan to improve data availability, analytics, and security.
  • Mechanisms: Azure Health Data Services, Google Cloud Healthcare API, and AWS HealthLake.
  1. Enhanced Data Privacy in Cloud Storage
  • Outline: In cloud storage frameworks, improve data confidentiality by creating robust approaches.
  • Goal: Concentrate on following confidentiality rules and securing private data.
  • Mechanisms: Azure, AWS, Differential privacy, and Homomorphic encryption.

How to run cloudsim examples in NetBeans?

Executing CloudSim instances in NetBeans is a compelling process that should be carried out by following several guidelines. To conduct this process in an efficient manner, we offer a procedural instruction explicitly:

Step 1: Install NetBeans IDE

  1. Download NetBeans: The current version of NetBeans IDE must be downloaded by visiting the NetBeans download page.
  2. Install NetBeans: Related to our operating system, we should adhere to the installation guidelines.

Step 2: Install Java Development Kit (JDK)

  1. Download JDK: Focus on downloading the JDK from the AdoptOpenJDK or Oracle JDK download page.
  2. Install JDK: Relevant to our operating system, the installation guidelines have to be followed.
  3. Set up NetBeans to utilize JDK:
  • The NetBeans should be opened.
  • We have to select Tools -> Java Platforms.
  • Choose the JDK installation directory by clicking Add Platform. Then, select Next and Finish.

Step 3: Download and Extract CloudSim

  1. Download CloudSim: Download the current version of CloudSim from the CloudSim GitHub repository.
  2. Extract CloudSim: To the specific directory, the downloaded ZIP file must be extracted.

Step 4: Develop a New Project in NetBeans

  1. Develop a New Java Project:
  • The NetBeans have to be opened.
  • We need to click File -> New Project.
  • Focus on choosing Java -> Java Application. Then, select Next.
  • For our project, provide a specific name (for instance: CloudSimExample). After that, select Finish.

Step 5: Append CloudSim Libraries to Our Project

  1. Include External JARs:
  • In the Projects window, we should right-click on our project.
  • Choose Properties.
  • It is important to select Libraries -> Add JAR/Folder.
  • The directory has to be accessed, in which the CloudSim is extracted. Choose all JAR files by opening the jars folder.
  • Plan to click Open and Ok.

Step 6: Build a New Java Class for CloudSim Instance

  1. Build a New Class:
  • In our project, we have to right-click on the Source Packages folder.
  • Choose New -> Java Class.
  • For the class, offer a particular name (for instance: CloudSimExample). Then, select Finish.

Step 7: Write CloudSim Sample Code

  1. Sample Code: Within our recently developed class, the subsequent example code has to be copied and pasted. By encompassing one cloudlet, one VM, one host, and one data center, this code configures a simple CloudSim simulation.

import org.cloudbus.cloudsim.allocationpolicies.VmAllocationPolicySimple;

import org.cloudbus.cloudsim.brokers.DatacenterBrokerSimple;

import org.cloudbus.cloudsim.cloudlets.Cloudlet;

import org.cloudbus.cloudsim.cloudlets.CloudletSimple;

import org.cloudbus.cloudsim.core.CloudSim;

import org.cloudbus.cloudsim.datacenters.Datacenter;

import org.cloudbus.cloudsim.datacenters.DatacenterSimple;

import org.cloudbus.cloudsim.hosts.Host;

import org.cloudbus.cloudsim.hosts.HostSimple;

import org.cloudbus.cloudsim.resources.Pe;

import org.cloudbus.cloudsim.resources.PeSimple;

import org.cloudbus.cloudsim.vms.Vm;

import org.cloudbus.cloudsim.vms.VmSimple;

import org.cloudsimplus.builders.tables.CloudletsTableBuilder;

import java.util.ArrayList;

import java.util.List;

public class CloudSimExample {

public static void main(String[] args) {

// Initialize the CloudSim library

CloudSim simulation = new CloudSim();

// Create a data center

Datacenter datacenter = createDatacenter(simulation);

// Create a broker

DatacenterBrokerSimple broker = new DatacenterBrokerSimple(simulation);

// Create a VM

Vm vm = new VmSimple(1000, 2); // 1000 MIPS, 2 PEs

vm.setRam(2048).setBw(1000).setSize(10000); // 2 GB RAM, 1 GB BW, 10 GB Storage

// Create a cloudlet

Cloudlet cloudlet = new CloudletSimple(10000, 2); // 10,000 MI, 2 PEs

cloudlet.setFileSize(300).setOutputSize(300); // 300 KB File and Output Size

// Submit VM and Cloudlet to broker

broker.submitVm(vm);

broker.submitCloudlet(cloudlet);

// Start the simulation

simulation.start();

// Print results

new CloudletsTableBuilder(broker.getCloudletFinishedList()).build();

}

private static Datacenter createDatacenter(CloudSim simulation) {

// Create a list of processing elements (PEs)

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

peList.add(new PeSimple(1000)); // 1000 MIPS per PE

// Create a host

Host host = new HostSimple(2048, 10000, 1000000, peList); // RAM, BW, Storage

host.setVmScheduler(new org.cloudbus.cloudsim.schedulers.vm.VmSchedulerTimeShared());

// Create a list of hosts

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

hostList.add(host);

// Create and return a data center

return new DatacenterSimple(simulation, hostList, new VmAllocationPolicySimple());

}

}

Step 8: Execute the CloudSim Instance

  1. Execute the Project:
  • In the Projects window, we need to right-click on the CloudSimExample class.
  • Then, choose Run File.
  • By demonstrating the aspects of the simulated cloudlet execution, the NetBeans Output window will exhibit the outcome.

Relevant to the cloud computing domain, several topic suggestions are provided by us, including concise outlines, explicit goals, and mechanisms. For assisting you to execute CloudSim instances in NetBeans, we offer a detailed guideline, along with a sample code.

IEEE Projects On Cloud Computing Topics & Ideas

Talk with ns3-code.com experts to know about the latest IEEE Projects On Cloud Computing Topics & Ideas, our developers carry in-depth research to guide you on the right path. As we stay updated on all emerging technologies we will be your best solution, chat with us to solve all your doubts.

  1. The Role and Potential Applications of Cloud Computing in the Banking Industry
  2. Study of data security and privacy preserving solutions in cloud computing
  3. A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments
  4. Cloud computing security in multi-clouds using Shamir’s secret sharing scheme
  5. Modeling and predicting fault tolerance in Vehicular Cloud Computing
  6. Toward Access Control Model for Context-Aware Services Offloaded to Cloud Computing
  7. On securing green’s function-based field simulation on public computing clouds
  8. Research on cloud computing data security model based on multi-dimension
  9. Analytical literature survey on existing load balancing schemes in cloud computing
  10. The core of constructing the future power systems computation platform is cloud computing
  11. Studying the effectiveness of using mobile cloud computing applications in learning
  12. Fast dynamic execution offloading for efficient mobile cloud computing
  13. Edge Cache-Aided Computation Offloading for Mobile Cloud Computing
  14. A Sufficient Way of Mass Data Storage for Cloud Computing Based on Hashing Strategy
  15. Cloud radio access networks (C-RAN) in mobile cloud computing systems
  16. An utility-based job scheduling algorithm for current computing Cloud considering reliability factor
  17. An intelligent analysis and prediction model for on-demand cloud computing systems
  18. Three step data security model for cloud computing based on RSA and steganography
  19. Ensuring data storage in cloud computing for distributed using high security password
  20. An energy consumption model and analysis tool for Cloud computing environments

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