Cloud Computing Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Cloud Computing ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Cloud Computing Projects
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Combining Lyapunov Optimization and Deep Reinforcement Learning for D2D Assisted Heterogeneous Collaborative Edge Caching
This project focuses on improving content sharing in wireless networks. Devices can share data directly with nearby devices or get it from a base station. The method uses smart learning to decide which devices should store and share content. It reduces delays, saves energy, and keeps the network running smoothly. -
Linking Digital Servitization and Industrial Sustainability Performance: A Configurational Perspective on Smart Solution Strategies
This project studies how manufacturing companies can help their customers become more sustainable using smart technologies. It looks at how artificial intelligence, focusing on outcomes, working with partners, and creating value together can improve customer performance. The researchers analyzed data from 180 Swedish firms to find strategies that work best. They identified five strategies, with one focused on using technology and partnerships to actively support customer operations. -
Unleashing Competitive Intelligence: News Mining Analysis on Technology Trends and Digital Health Driving Healthcare Innovation
This project focuses on helping healthcare businesses understand trends in digital health using technology. It collects information from public news and event data. The system uses language processing to find important topics and connections in the data. This helps companies make better decisions and keep up with new developments like telehealth or precision medicine. -
Attribute-Based Searchable Encryption With Forward Security for Cloud-Assisted IoT
This project focuses on securely storing and searching data in the cloud. It allows the data owner to delete outdated files by themselves without relying on anyone else. The system also ensures that deleted data cannot be accessed or searched again. The researchers tested the method and showed that it is both safe and efficient. -
Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing
This project focuses on improving how cloud systems handle many incoming tasks from industrial IoT devices. It develops a new algorithm called OSAPSO that combines different optimization techniques to make task scheduling faster and more efficient. The method reduces energy use, cost, and delays while increasing system performance. Experiments show it works better than existing approaches in real cloud environments. -
Privacy-Preserving and Trusted Keyword Search for Multi-Tenancy Cloud
This project focuses on making cloud data easier and safer to search. It allows users to search for keywords across multiple independent cloud tenants without risking privacy. The system checks that the search results are correct and keeps users accountable. It also speeds up searches by processing data segments in parallel. -
Secure and Fine-Grained Access Control With Optimized Revocation for Outsourced IoT EHRs With Adaptive Load-Sharing in Fog-Assisted Cloud Environment
This project focuses on keeping patient health records safe when stored and shared through IoT devices and the cloud. It uses blockchain to check who can access the data and fog computing to handle heavy encryption tasks. The system allows easy removal of users and ensures fast, secure data sharing. Tests show it works efficiently while protecting sensitive information. -
A C-ITS Architecture for MEC and Cloud Native Back-End Services
This project builds a smart system that helps vehicles communicate quickly and safely using cloud and edge computing. It connects cars, nearby servers, and cloud systems to share traffic and road data in real time. The design reduces delays and supports many connected vehicles at once. It was tested to ensure smooth and scalable communication between all parts. -
A Cloud Monitor to Reduce Energy Consumption With Constrained Optimization of Server Loads
This project focuses on improving how cloud computing systems use energy and resources. It uses a mathematical model to decide how jobs should be shared among servers. The goal is to reduce waiting time, save energy, and make servers work more efficiently. Simulations show that this method can cut energy use by up to 42%. -
A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds
This project develops a cloud system that works close to users, at the edge of the network. It creates virtual clouds on physical machines and shares resources like CPU, memory, and storage efficiently. Each virtual cloud stays separate, keeping data and operations isolated. Users can also switch between different virtual clouds safely, while the system manages security automatically. -
An Adaptive Threshold-Based Modified Artificial Bee Colony Optimization Technique for Virtual Machine Placement in Cloud Datacenters
This project focuses on making cloud computing more energy-efficient. It introduces a new method to place virtual machines on physical servers in a way that reduces energy use. The approach uses a smart optimization technique to find underused servers and decide the best way to allocate resources. Simulations show it performs better than existing methods with high accuracy and precision. -
An Efficient FHE-Enabled Secure Cloud–Edge Computing Architecture for IoMT Data Protection With Its Application to Pandemic Modeling
This project looks at using smart medical devices to study how diseases like COVID-19 spread. It focuses on keeping people’s data private while still letting researchers simulate disease transmission. The team created a secure system that allows data to be analyzed without exposing personal information. They tested it and showed it works accurately and safely for pandemic modeling. -
Assessing Performance of Cloud-Based Heterogeneous Chatbot Systems and A Case Study
This project studies how cloud-based chatbots perform when interacting with humans and automated users. It measures key factors like response time, the number of requests handled, and system behavior under heavy load or connection issues. The goal is to help developers understand chatbot performance and make better choices for building and deploying them in cloud environments. The study also compares different ways to assess these systems effectively. -
ATHENA: An Intelligent Multi-x Cloud Native Network Operator
This project develops Athena, a new system for managing modern mobile networks like 4G and 5G. It makes networks more flexible, efficient, and easy to control across different vendors and devices. Athena reduces operational overhead, saves energy, and keeps the network highly reliable. The system was tested and shown to improve speed, performance, and sustainability. -
Attribute-Based Management of Secure Kubernetes Cloud Bursting
This project focuses on making cloud computing more secure and flexible. It shows a way to move workloads between clouds safely using Kubernetes and special encryption. The system keeps data protected while making cloud operations easier and faster. A working example proves that this method is practical and effective. -
Blockchain-Based Decentralized Storage Design for Data Confidence Over Cloud-Native Edge Infrastructure
This project focuses on building a new way to store data across many devices instead of in one central place. It uses cloud and blockchain ideas to make storage faster, more secure, and private. The system works well on edge devices and improves data transfer compared to existing methods. Overall, it solves common problems in decentralized data management. -
CFWS: DRL-based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers
This project focuses on making cloud data centers more energy-efficient and environmentally friendly. It uses artificial intelligence to decide when and where to move virtual machines across data centers. This helps reduce electricity use and carbon emissions while keeping services running smoothly. The system also minimizes unnecessary machine movements, saving time and energy. -
Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques Recent Research Advancements
This project studies ways to keep cloud networks safe from unusual or harmful activity. It looks at how machine learning and deep learning can detect problems like intrusions or attacks. The research compares different methods and suggests better ways to find anomalies. The goal is to make cloud networks more secure and reliable. -
Cloud Radio Random-Access Networks With Multi-Packet Reception Capability
This project studies a new way to manage mobile networks using cloud computing. It moves some base station functions to the cloud, which reduces hardware and costs. The researchers developed a method to improve data transmission and measured its performance. Their results show that this approach can make networks faster and more efficient. -
Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting
This project focuses on making a powerful AI method called spectral clustering safer and easier to use for small data owners. It allows users to combine data and use cloud computing without sharing their private information. The method encrypts the data so that clustering can be done securely on the cloud. Users get accurate results while keeping their data fully private.
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How We Help You with Cloud Computing Projects
At UniPhD, we provide complete guidance and support for Cloud Computing ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Cloud Computing Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Cloud Computing research and thesis development. We offer fast-track dissertation writing services to help you complete your Cloud Computing thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Cloud Computing Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Cloud Computing final year project.
Cloud Computing Research Support for PhD Scholars
UniPhD offers advanced Cloud Computing research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.