In the domain of cloud computing, several projects that are emerging in current years. Ranging from simple theories to progressive implementations, these projects encompass a broad scope of cloud computing topics, and are formulated to assist students to acquire realistic expertise and expand their interpretation of cloud mechanisms:

Beginner Level

1. Personal Website Hosting

• Goal: Through the utilization of cloud services, host a portfolio or personal website.
• Significant Concepts: Domain management, static website hosting, CDN
• Cloud Provider: Azure Blob Storage, AWS S3 and CloudFront, Google Cloud Storage.

2. Online File Storage System

• Goal: Specifically, for safe file upload, storage, and recovery, aim to construct a simple application.
• Significant Concepts: File management, object storage, user authentication.
• Cloud Provider: Azure Blob Storage, AWS S3, Google Cloud Storage.

3. Serverless To-Do List Application

• Goal: By employing serverless infrastructure, develop a basic to-do list application.
• Significant Concepts: User authentication, Serverless computing, NoSQL databases.
• Cloud Provider: Azure Functions and Cosmos DB, AWS Lambda and DynamoDB, Firebase.

4. Cloud-Based Chat Application

• Goal: It is approachable to construct an actual-time chat application.
• Significant Concepts: User authentication, real-time database, WebSockets.
• Cloud Provider: Azure SignalR Service, Firebase Realtime Database, AWS AppSync.

5. Website Traffic Analysis

• Goal: Through utilizing cloud services, focus on monitoring and examining traffic.
• Significant Concepts: Visualization, data collection, analytics.
• Cloud Provider: Azure Monitor, Google Analytics, AWS CloudWatch.

Intermediate Level

6. Blog Hosting Platform

• Goal: Mainly, for hosting websites or blogs, aim to develop an environment.
• Significant Concepts: Database management, content management, user authentication.
• Cloud Provider: Azure Web Apps, Cosmos DB; AWS S3, EC2, RDS; Google Cloud App Engine, Firestore.

7. Simple E-Commerce Website

• Goal: Including shopping cart and product catalog, it is appreciable to create an e-commerce website.
• Significant Concepts: Payment integration, web hosting, database management.
• Cloud Provider: Azure Web Apps, Cosmos DB; AWS S3, EC2, RDS; Google Cloud App Engine, Firestore.

8. Image Processing and Storage

• Goal: Generally, for uploading, processing, and saving images, develop a web application.
• Significant Concepts: Serverless functions, image processing, object storage.
• Cloud Provider: Azure Functions and Blob Storage, AWS Lambda and S3, Google Cloud Functions and Cloud Storage.

9. Weather Data Collection and Analysis

• Goal: In order to gather and examine weather data, focus on constructing an application.
• Significant Concepts: Data exploration, API combination, data storage.
• Cloud Provider: Azure, AWS, Google Cloud.

10. DevOps CI/CD Pipeline

• Goal: It is significant to configure a continuous integration and continuous deployment (CI/CD) pipeline.
• Significant Concepts: Version control, automated testing, build and deployment automation.
• Cloud Provider: Google Cloud Build, AWS CodePipeline, Azure DevOps.

Advanced Level

11. Real-Time Traffic Analysis System

• Goal: A framework has to be developed to track and explore traffic data in actual-time.
• Significant Concepts: Data visualization, data streaming, real-time processing.
• Cloud Provider: Azure Stream Analytics, AWS Kinesis, Google Cloud Pub/Sub.

12. Edge Computing with Cloud Integration

• Goal: As a means to process data regionally before transmitting to the cloud, it is beneficial to execute an edge computing approach.
• Significant Concepts: Data synchronization, edge computing, IoT.
• Cloud Provider: Azure IoT Edge, AWS Greengrass.

13. Cloud-Based Inventory Management System

• Goal: Typically, to handle inventory rates, orders, and provider information, create an appropriate framework.
• Significant Concepts: Supplier management, inventory tracking, order management.
• Cloud Provider: Azure Web Apps, Cosmos DB; Google Cloud App Engine, Firestore, AWS EC2, RDS, S3, Lambda, Cognito.

14. Cloud-Based CRM System

• Goal: A customer relationship management framework has to be constructed, which is facilitated by the cloud environment.
• Significant Concepts: Email marketing, contact management, sales tracking.
• Cloud Provider: Azure Web Apps, Cosmos DB; AWS EC2, RDS, S3, Elastic Beanstalk, SES; Google Cloud App Engine, Firestore.

15. Online Examination System

• Goal: For carrying out exams and evaluations, aim to develop an online examination environment.
• Significant Concepts: Automated grading, test creation, student authentication.
• Cloud Provider: Azure Web Apps, Cosmos DB; AWS EC2, RDS, S3, Lambda, CloudFront; Google Cloud App Engine, Firestore.

16. Cloud-Based Video Streaming Service

• Goal: To stream videos encompassing cloud-related storage and delivery, create an efficient environment.
• Significant Concepts: CDN delivery, video encoding, storage.
• Cloud Provider: Azure Media Services; AWS Media Services, S3, CloudFront, Lambda; Google Cloud Media Services.

17. Serverless Data Pipeline

• Goal: Specifically, for ETL (Extract, Transform, Load) procedures, focus on deploying a serverless data pipeline.
• Significant Concepts: Data extraction, transformation, loading into data warehouse.
• Cloud Provider: Azure Functions, Data Factory, SQL Data Warehouse; AWS Lambda, Glue, S3, Redshift; Google Cloud Functions, Dataflow, BigQuery.

18. Cloud-Based Payroll System

• Goal: For managing staff logs and payments, aim to develop a payroll management model.
• Significant Concepts: Tax management, employee management, salary calculation.
• Cloud Provider: Azure Web Apps, Cosmos DB; AWS EC2, RDS, S3, Lambda; Google Cloud App Engine, Firestore.

19. Blockchain Integration with Cloud Services

• Goal: Through the utilization of cloud services, execute blockchain approaches.
• Significant Concepts: Secure data sharing, decentralized applications, smart contracts.
• Cloud Provider: Azure Blockchain Service, AWS Managed Blockchain, Google Cloud Blockchain approaches.

20. IoT Data Management in the Cloud

• Goal: For handling and investigating IoT data with the aid of cloud services, focus on building a framework.
• Significant Concepts: Actual-time analytics, IoT device management, data streaming.
• Cloud Provider: Google Cloud IoT, AWS IoT Core, Azure IoT Hub.

Emerging Technologies

21. Machine Learning Model Deployment

• Goal: For forecasting or categorization missions, it is appreciable to implement a machine learning framework.
• Significant Concepts: API combination, model training, interpretation.
• Cloud Provider: Azure Machine Learning, AWS SageMaker, Google AI Platform.

22. Big Data Analytics Platform

• Goal: An environment has to be developed mainly for processing and examining extensive datasets.
• Significant Concepts: Processing pipelines, visualization, data incorporation, data warehousing.
• Cloud Provider: Azure HDInsight, AWS EMR, Google BigQuery.

23. Quantum Computing in the Cloud

• Goal: Focus on investigating the purpose of quantum computing sources offered by cloud environments.
• Significant Concepts: Hybrid quantum-classical implementations, quantum methods.
• Cloud Provider: IBM Quantum Experience, AWS Braket.

24. Federated Learning in Cloud

• Goal: In order to instruct frameworks among decentralized data resources, deploy a federated learning model.
• Significant Concepts: Distributed machine learning, federated learning, data privacy.
• Cloud Provider: AWS SageMaker, Google AI Platform.

25. Secure Multi-Cloud Environment

• Goal: For handling protection among numerous cloud suppliers, construct suitable tools and policies.
• Significant Concepts: Security strategies, compliance, multi-cloud management.
• Cloud Provider: Azure, AWS, Google Cloud.

What are the algorithms in cloud computing?

There are numerous methods that exist in the field of cloud computing, but some are determined as efficient and crucial. We offer few significant methods employed in cloud computing:

1. Resource Management Algorithms

Dynamic Resource Allocation

• Goal: To enhance expense and effectiveness, it allots sources on the basis of the recent requirements in a dynamic manner.
• Instance Methods:
• Round Robin: Among every mission, this algorithm shares sources equally.
• Min-Min: To the rapid resource accessible, it allocates the smallest mission.
• Max-Min: Mainly, to the quick resource accessible, Max-Min allocates the extensive mission.
• Ant Colony Optimization: To identify the smallest routes, it employs the activity of ants, and focuses on allocating sources in an effective way.

Load Balancing

• Goal: Among numerous servers, share incoming network traffic equally.
• Instance Methods:
• Round Robin: By means of servers, round robin iterates in sequential manner.
• Least Connections: Through using the least active connections, it leads traffic to the server.
• Weighted Round Robin: This method is capable of allocating weights to servers and shares congestion in an appropriate way.
• Dynamic Load Balancing: On the basis of the actual-time server performance parameters, dynamic load balancing adapts distribution.

Auto-Scaling

• Goal: According to the load, adapts the number of active servers in an automatic manner.
• Instance Methods:
• Threshold-Based Scaling: On the basis of predetermined thresholds (For instance., CPU utilization), this method stimulates scaling activities.
• Predictive Scaling: To predict requirement and scale, it employs predictive analytics and historical data.
• Reinforcement Learning-Based Scaling: In order to improve scaling choices, this algorithm implements reinforcement learning.

2. Security Algorithms

Data Encryption

• Goal: The main objective of data encryption is to secure data morality and privacy.
• Instance Methods:
• AES (Advanced Encryption Standard): Generally, AES is a symmetric encryption method that is employed in an extensive manner.
• RSA (Rivest-Shamir-Adleman): This method is usually utilized and is determined as an asymmetric encryption method.
• Elliptic Curve Cryptography (ECC): By means of smaller key sizes, it offers robust protection.

Intrusion Detection

• Goal: The process of identifying and reducing illicit access and assaults are examined as a significant aim of intrusion identification.
• Instance Methods:
• Signature-Based Detection: Through comparing familiar assault trends, it is capable of detecting attacks.
• Anomaly-Based Detection: By means of employing machine learning methods such as neural networks, K-Means, and SVM, identifies variations from usual activity.
• Hybrid Detection: Specifically, for enhanced precision, this method integrates anomaly-based and signature-based algorithms.

Access Control

• Goal: The main objective is to assure that only authoritative users are allowed to access sources.
• Instance Methods:
• Role-Based Access Control (RBAC): On the basis of user roles, RBAC allocates consents.
• Attribute-Based Access Control (ABAC): According to the variables such as time of day, user role, this method offers permission.
• Multi-Factor Authentication (MFA): To improve protection, employs numerous approaches of authentication.

3. Data Processing Algorithms

MapReduce

• Goal: Among a distributed computing platform, focus on processing extensive datasets simultaneously.
• Components:
• Map Function: It creates key-value pairs by processing input data.
• Reduce Function: From the map function, it gathers and processes key-value pairs.

Data Deduplication

• Goal: To enhance performance and conserve storage space, aim to remove replicate data.
• Instance Methods:
• Chunk-Based Deduplication: The main focus of this method is to divide data into segments and remove replicate data.
• File-Level Deduplication: Through contrasting file signatures like hashes, it eliminates redundant files.

Stream Processing

• Goal: The main aim is to process data streams in an actual-time.
• Instance Methods:
• Apache Storm: This method is referred to as distributed actual-time computation model.
• Apache Flink: It is defined as a stream processing model including active computation.

4. Network Management Algorithms

Software-Defined Networking (SDN)

• Goal: Through the utilization of software-related controllers, essentially handle and enhance network sources.
• Instance Methods:
• Shortest Path Algorithms: It is significant for identifying the shortest path in a network. Specifically, it is determined as Dijkstra’s method.
• Flow Scheduling Algorithms: To decrease traffic, it directs network flows in a perfect manner.

Traffic Engineering

• Goal: Through dynamically examining, forecasting, and controlling the activity of data shared, focus on enhancing the effectiveness of functional networks.
• Instance Methods:
• Multi-Protocol Label Switching (MPLS): On the basis of the short path labels instead of extensive network addresses, this method guides data from one network node to the next in an effective manner.
• Traffic Load Balancing: To share network congestion among numerous paths, these methods are employed.

5. Machine Learning and AI Algorithms

Predictive Analytics

• Goal: According to historical data, it focuses on forecasting upcoming patterns.
• Instance Methods:
• Linear Regression: Generally, on the basis of the linear connection with one or more independent attributes, linear regression forecasts dependent attributes.
• Decision Trees: It is capable of designing choices and their potential impacts.
• Neural Networks: To identify trends and create forecasting, neural networks are employed. Generally, it is complicated frameworks motivated by the human brain.

Anomaly Detection

• Goal: Abnormal trends have to be detected, which do not comply with anticipated activity.
• Instance Methods:
• Isolation Forest: Through segregating examinations, isolation forest identifies abnormalities.
• Autoencoders: For unsupervised anomaly identification, it is employed and is examined as a type of neural networks.

6. Cloud Storage Management Algorithms

Distributed File Systems

• Goal: To assure availability and consistency, aim to handle data among numerous storage nodes.
• Instance Methods:
• Google File System (GFS): Among numerous nodes, handles extensive distributed data sets.
• Hadoop Distributed File System (HDFS): Normally, HDFS is modelled for big data analytics. It is a scalable, fault-tolerant file framework.

Data Replication

• Goal: Through saving copies of data on numerous servers, it assures data consistency and accessibility.
• Instance Methods:
• Primary-Backup Replication: From an initial server, data is copied to one or more backup servers by employing this method.
• Quorum-Based Replication: Through demanding a wide range of nodes to coordinate with upgrades, it assures data reliability.

7. Scheduling Algorithms

Task Scheduling

Goal: In order to enhance resource utilization and effectiveness, focus on planning missions in an effective manner.

Instance Methods:

• First-Come, First-Served (FCFS): Based on the arrival order, it plans missions accordingly.
• Shortest Job Next (SJN): By considering the shortest implementation time first, it plans the missions.
• Genetic Algorithms: To identify best planning approaches, it utilizes evolutionary methods.

Cloud Computing Thesis for CSE

Have matlabprojects.org by your side we will provide you novel research assistance for all concepts of cloud computing, get thesis ideas and topics that matches with your interest. Read the topics that we have worked on cloud previously.  We excel in creating a strong cloud computing approach that is customized to fit your specific research goals, guaranteeing reliability by integrating suitable data collection methods, analytical tools, and ethical standards. Additionally, we provide you with the references we used for your cloud research, so don’t hesitate to collaborate with our team.

1. Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System
2. Research of College Computer Laboratory Based on Cloud Computing Technology
3. Modelling and Analysis of Cloud Computing Systems using Queuing Models with Correlated Arrivals and Correlated Reneging
4. Implementation of K-means clustering for evaluating SaaS on the cloud computing environment
5. Research of Security Situational Awareness and Visualization Approach in Cloud Computing
6. Analysing Security and Privacy Management for Cloud Computing Environment
7. Mobile cloud computing usage for onboard vehicle servers in collecting disaster data information
8. Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing
9. Game Theoretic Approach for Real-Time Task Scheduling in Cloud Computing Environment
10. Performance Modeling of Concurrent Live Migration Operations in Cloud Computing Systems Using PRISM Probabilistic Model Checker
11. jMonAtt: Integrity Monitoring and Attestation of JVM-Based Applications in Cloud Computing
12. Remote consulting for product EMC compliance by means of virtual workspace and cloud computing
13. Improving Health Care by Help of Internet of Things and Bigdata Analytics and Cloud Computing
14. Identity management based security architecture of cloud computing on multi-agent systems
15. Implementation of Big Data in Cloud Computing with optimized Apache Hadoop
16. Enhancing the security of cloud computing: Genetic algorithm and QR code approach
17. Construction of Accounting Technology Block Analysis System Under the Background of Big Data Cloud Computing
18. Automatic control of the quality of service contract by a third party in the Cloud Computing
19. Research of digital community service platform based on cloud computing
20. Research on Load Prediction Based on Improve GWO and ELM in Cloud Computing