Final Year Project Ideas Cloud Computing that can be established in your research are listed in this page. We help in shaping your research career by providing best project guidance from sharing of novel topics to publication. Get your research done under single roof.
Significant Cloud Computing Research Topics & Plans (2025)
Including developments in security, edge computing, and AI, the cloud computing domain has emerged in a constant manner. For cloud computing in 2025, we suggest few research topics and plans, which are significant and interesting:
- Cloud Security & Privacy
- AI-Based Threat Detection in Cloud Platforms
- Security Challenges in Cloud-Based IoT
- Zero Trust Architecture for Cloud
- Data Privacy in Multi-Cloud Deployments
- Homomorphic Encryption for Secure Cloud Data Processing
- Intrusion Detection and Prevention Systems (IDPS) for Cloud
- Blockchain-Based Cloud Security
- Secure Multi-Tenancy in Cloud Environments
- Research Plan:
“Implementing Blockchain for Secure Data Storage in Multi-Tenant Cloud Environments”
- Assure tamper-proof and decentralized storage security with the aid of blockchain.
- Cloud Performance Optimization
- Edge Computing vs Cloud Computing Performance Evaluation
- Containerization vs Virtualization Performance Analysis
- Reducing Latency in Cloud Gaming Services
- AI-Driven Auto-Scaling in Cloud Platforms
- Performance Analysis of Cloud-Based Deep Learning Models
- Load Balancing Strategies for Large-Scale Cloud Systems
- Dynamic Resource Allocation in Cloud Data Centers
- Research Plan:
“AI-Based Load Balancing Algorithm for Scalable Cloud Applications”
- As a means to forecast and adapt resource allocation in actual-time, an AI-powered load balancer has to be applied.
- Cloud Computing & Green Energy
- AI-Driven Power Management in Cloud Infrastructure
- Energy-Aware Virtual Machine (VM) Placement
- Carbon Footprint Reduction in Cloud Computing
- Renewable Energy-Powered Cloud Data Centers
- Energy-Efficient Cloud Computing Architectures
- Research Plan:
“Optimizing Cloud Workloads for Reduced Energy Consumption”
- To reduce energy consumption in addition to preserving functionality, we aim to create a scheduling algorithm.
- Cloud & Artificial Intelligence (AI)
- Autonomous Cloud Management Using Reinforcement Learning
- AI-Powered Cloud Cost Optimization
- Explainable AI in Cloud Security
- AI-Based Cloud Service Orchestration
- Federated Learning in Cloud Computing
- Research Plan:
“Applying Federated Learning for Privacy-Preserving AI in Cloud-Based IoT Systems”
- In the cloud, process IoT data in a safer and efficient manner by applying federated learning.
- Cloud & Edge Computing
- Containerized Edge Computing for 5G Applications
- Cloud vs Edge Computing for 6G Networks
- AI-Optimized Task Scheduling in Edge Computing
- Hybrid Cloud-Edge Architectures for Low Latency Applications
- Research Plan:
“Comparative Analysis of Cloud vs Edge for Real-Time Data Processing”
- For smart cities and autonomous vehicles, various data processing models have to be simulated.
- Serverless Computing & Function as a Service (FaaS)
- AI-Based Workload Prediction for Serverless Environments
- Cost Optimization of Serverless Workloads
- Security Challenges in Serverless Applications
- Optimizing Function Cold Start in Serverless Computing
- Research Plan:
“Improving Performance of AI Workloads in Serverless Cloud Environments”
- Specifically for serverless functions managing AI missions, we plan to create an intelligent pre-warming technology.
- Cloud Networking & 6G
- Cloud SDN (Software-Defined Networking) Enhancements
- AI-Based Traffic Engineering for Cloud Networks
- Cloud-Based Network Slicing for 6G
- 5G/6G Cloud Networks
- Research Plan:
“AI-Based Cloud Network Traffic Optimization for 6G”
- To improve cloud network throughput and effectiveness, a machine learning-related traffic forecasting model must be created.
- Cloud Storage & Big Data
- Secure Data Sharing Mechanisms in Cloud
- Big Data Analytics in Multi-Cloud Environments
- Data Deduplication Techniques for Cloud Storage
- Quantum Computing for Cloud Data Processing
- Research Plan:
“AI-Based Data Deduplication for Reducing Cloud Storage Costs”
- By means of deep learning, an intelligent data deduplication system should be deployed.
- Cloud in Healthcare & Smart Cities
- Real-Time Cloud Analytics for Smart Cities
- Cloud-Based Disaster Management Systems
- AI-Driven Smart Healthcare in Cloud Environments
- Cloud-Enabled E-Health Systems
- Research Plan:
“Cloud-Based AI Model for Remote Patient Monitoring”
- For early disease forecasting, we intend to create an AI-powered cloud-related health tracking system.
- Multi-Cloud & Cloud Interoperability
- Data Migration Strategies Between Cloud Providers
- Load Balancing Across Multi-Cloud Platforms
- Inter-Cloud Resource Scheduling Optimization
- Research Plan:
“Blockchain-Enabled Secure Inter-Cloud Communication”
- To protect data sharing among several cloud providers, a blockchain-related framework has to be deployed.
- Quantum Cloud Computing
- Quantum Cryptography for Cloud Data Protection
- Quantum Machine Learning on Cloud Platforms
- Quantum Algorithms for Cloud Security
- Research Plan:
“Quantum-Assisted AI Model Training in Cloud Computing”
- In cloud-related environments, speed up AI training by utilizing quantum computing.
Research Approach for These Projects
- Introduction:
- Research Context: On the particular cloud computing topic, we have to offer context and background details.
- Issue Statement: The issue has to be explained, which our research plans to solve.
- Goals: The goals of our project should be summarized.
- Research Queries: The major research queries have to be expressed.
- Scope and Shortcomings: The scope and any shortcomings must be described.
- Literature Survey:
- Summary: Relevant to our project topic, a survey of present literature has to be carried out.
- Theoretical Framework: Important concepts and designs should be considered.
- Gap Analysis: In the literature, find gaps which we intend to address.
- Relevant Work: Associated to our topic, major research must be outlined.
- Research Model:
- Research Methodology: Plan to determine our research methodology, and it could be quantitative, qualitative or mixed-techniques.
- Research Type: Our research type has to be specified, such as exploratory, descriptive, explanatory, or evaluative.
- Hypotheses: If our study is hypothesis-based, hypotheses should be designed.
- Approach:
- Data Gathering:
- Primary Data: The process of gathering primary data has to be defined (instance: surveys, experiments).
- Secondary Data: Secondary data sources must be explained (instance: current datasets).
- Tools and Mechanisms: The suitable environments, mechanisms, and tools have to be determined.
- Experimental Configuration: The arrangement and configuration has to be described.
- Data Analysis:
- Methods: The data analysis methods should be described.
- Software: For data analysis, the software tools have to be declared.
- Verification: The process of verifying our outcomes must be defined.
- Execution:
- Development Procedure: In the application, the involved procedures should be explained.
- Algorithms and Designs: Any frameworks, designs, or algorithms have to be described, which we employ or build in our research.
- Technical Issues: Any technical issues should be examined. The process of solving them must be described.
- Outcomes and Discussion:
- Outcomes Presentation: By employing tables, graphs, and charts, our discoveries should be demonstrated.
- Analysis: In the scenario of our research queries, the outcomes have to be examined.
- Comparison: With current research or benchmarks, our outcomes should be compared.
- Discussion: The outcomes must be analyzed, and their impacts have to be considered.
- Conclusion:
- Overview: The major discoveries should be outlined.
- Contributions: The offerings of our project have to be emphasized.
- Upcoming Work: For future study, we need to recommend areas.
- References:
- Citations: By adhering to a consistent citation style (instance: IEEE, APA), all the utilized references and sources have to be mentioned.
What is an example of a cloud simulator?
CloudSim is one of the best instances of a cloud simulator, which is employed across various domains in an extensive manner. About CloudSim, we offer an outline, major characteristics, application areas, along with other cloud simulators:
CloudSim Outline:
At the University of Melbourne, the CloudSim is created by the Cloud Computing and Distributed Systems (CLOUDS) Laboratory. It is considered as an extensively utilized open-source framework. Using this framework, cloud computing platforms can be designed, simulated, and tested. It encompasses power management, resource allocation strategies, virtual machines (VMs), and data centers.
Major Characteristics:
- Modeling and Simulation: Different cloud computing elements such as users, data centers, and VMs can be simulated using CloudSim.
- Resource Management: This framework enables various methods for resource provisioning and scheduling.
- Power Management: Energy-effective cloud data centers can be simulated by means of CloudSim.
- Scalability: Extensive simulations of cloud platforms can be assisted.
- Cost Analysis: Various pricing designs of cloud services can be assessed through CloudSim.
Application Areas:
- It supports examining cloud scheduling algorithms.
- In cloud data centers, the energy effectiveness should be examined.
- Infrastructure as a Service (IaaS) environments must be simulated.
- In cloud computing, the network functionality has to be assessed.
Other Cloud Simulators:
- iFogSim: For fog computing simulations, it expands CloudSim.
- EdgeCloudSim: Mobile cloud platforms and edge computing are the major concentrations.
- GreenCloud: In energy-effective cloud simulations, it is more attentive.
In terms of cloud computing, we recommended significant research topics and plans, which are ideal in 2025. Regarding the CloudSim framework, a concise outline is provided by us, including its major characteristics, application areas, and other cloud simulators.
Final Year Thesis Ideas Cloud Computing
Final Year Thesis Ideas Cloud Computing that is very innovative are listed by us, we will provide you with complete project guidance. We are in this field for more than 15+ years and have done more than 2500+ Cloud Computing projects let us take you to higher grade.
- Medical image segmentation method based on multi-feature interaction and fusion over cloud computing
- An energy-aware combinatorial auction-based virtual machine scheduling model and heuristics for green cloud computing
- Efficient Secure Aware Scheduling Model for Enhancing Security and Workflow Model in Cloud Computing
- Evolutionary trends in progressive cloud computing based healthcare: Ideas, enablers, and barriers
- A Hybrid, Distributed Condition Monitoring System using MEMS Microphones, Artificial Neural Networks, and Cloud Computing
- Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey
- A bi-objective workflow scheduling in virtualized fog-cloud computing using NSGA-II with semi-greedy initialization
- Cloud computing-oriented big data analysis-based intelligent university talent development mechanism
- SD-SRF: An Intelligent Service Deployment Scheme for Serverless-operated Cloud-Edge Computing in 6G Networks
- E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing
- Run-time failure detection via non-intrusive event analysis in a large-scale cloud computing platform
- Integrated smart analytics of nucleic acid amplification tests via paper microfluidics and deep learning in cloud computing
- Review on QoS and security challenges associated with the internet of vehicles in cloud computing
- Optimal Scheduling Method of Information System Operation and Maintenance Resources Based On Data Perception in Cloud Computing
- Building knowledge ambidexterity using cloud computing: Longitudinal case studies of SMEs experiences
- Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT
- Construction of Supply Chain Security Management Model for Information and Communication Technology Based on the Internet of Things and Cloud Computing
- Optimal multi-user offloading with resources allocation in mobile edge cloud computing
- Secure authentication schemes in cloud computing with glimpse of artificial neural networks: A review
- Probing the factors influencing cloud computing adoption in healthcare organizations: A three-way interaction model
