Cloud Computing becomes a more prevalent and research-worthy area, as it often emerges with innovative theories, novel aspects and modernized techniques. We suggest numerous modern topics on cloud computing which are optimal for performing detailed research:

1. Edge and Fog Computing

• Explanation: To enhance actual-time functional capacities and decrease response time, explore the synthesization of edge and fog computing with cloud models.
• Significant Areas:
• On the edge, it includes resource management and allocation tactics.
• Automated systems, smart cities and IoT are the applicable areas.
• Security and secrecy in edge computing.

2. Serverless Computing

• Explanation: At where the cloud provider handles the models, investigate the advantages and problems of serverless networks. These serverless networks access the developers to concentrate on code.
• Significant Areas:
• Event-driven computing and actual-time applications.
• Financial management and performance enhancement.
• Security and segregation of serverless processes.

3. AI and Machine Learning Integration

• Explanation: In order to prepare and implement AI (Artificial Intelligence) and ML (Machine Learning) frameworks on a large scale, utilize cloud services.
• Significant Areas:
• AI-powered cloud resource management.
• Privacy-preserving AI and federated learning.
• Hyperparameter tuning and scalable model training.

4. Multi-Cloud and Hybrid Cloud Strategies

• Explanation: Among diverse cloud providers and hybrid platforms, handle the resources and load densities in a dynamic manner by creating effective tactics.
• Significant Areas:
• Allocation of load-densities and financial efficiency.
• Security and adherence in multi-cloud configurations.
• Data flexibility and compatibility.

5. Cloud Security

• Explanation: In opposition to illicit access, virtual threats and cyber-assaults, improve the security of cloud services.
• Significant Areas:
• Access management techniques and zero-trust security frameworks.
• Data secrecy and enhanced encryption methods.
• Prevention and AI-driven intrusion detection systems.

6. Data Management and Big Data Analytics

• Explanation: Regarding the cloud platforms, accumulate, perform and evaluate huge amounts of data effectively.
• Significant Areas:
• Data lakes and adaptable data storage findings.
• Synthesization of cloud data services with ML (Machine Learning) and AI (Artificial Intelligence).
• Actual-time big data processing and analytics.

7. Cloud Performance Optimization

• Explanation: The performance and capability of cloud services and applications are enhanced through this research.
• Significant Areas:
• Performance evaluations and surveillance.
• Effective resource utilization and auto-scaling.
• Performance tuning and load balancing.

8. Quantum Computing in the Cloud

• Explanation: For the purpose of addressing complicated issues, investigate the application of quantum computing resources which is offered by cloud vendors.
• Significant Areas:
• Synthesization of quantum and conventional computing resources.
• For cloud platforms, it involves the design of quantum techniques.
• Applicable areas are machine learning, cryptography and improvements.

9. Blockchain and Distributed Ledger Technology (DLT)

• Explanation: To improve integrity, clarity and security in cloud services, the application of blockchain and DLT (Distributed Ledger Technology) should be explored.
• Significant Areas:
• Identity management and blockchain-based access control mechanisms.
• Especially for automated cloud service management, apply smart contracts.
• Decentralized cloud storage and computing.

10. Green Cloud Computing

• Explanation: Decrease energy usage and greenhouse gas emission by creating eco-friendly cloud computing findings.

What are the hot topics for research nowadays in cloud computing?

In the current environment, cloud computing is one of the trending domains which incorporate a wide range of topics for in-depth exploration. By concentrating on data analysis in cloud computing, few significant and novel research topics are provided by us for thesis writing:

1. Scalable Data Analytics Platforms

• Topic: Design and Implementation of Scalable Data Analytics Platforms in Cloud Environments
• Explanation: In a cloud platform, manage an extensive amount of data by investigating techniques for the purpose of developing a scalable data analytics environment.
• Main Areas: Data partitioning strategies, distributed data processing and cloud storage optimization.

2. Real-Time Data Analytics

• Topic: Real-Time Data Stream Processing and Analytics in the Cloud
• Explanation: For real-time processing and interpretation of data streams in cloud platforms, explore algorithms which primarily concentrate on high-throughput demands and minimal latency.
• Main Areas: Real-time analytics algorithms, applicable areas in Iot and finance and Stream processing models.

3. Big Data Analytics

• Topic: Enhancing Big Data Analytics Performance Using Cloud Computing
• Explanation: Utilize cloud computing resources to explore the paths for enhancing the performance of big data analytics. The applications of distributed computing and cloud-native technologies are incorporated.
• Main Areas: Performance enhancement, Spark, cloud-native big data tools and Hadoop.

4. Predictive Analytics

• Topic: Predictive Analytics in Cloud Environments for Improved Decision-Making
• Explanation: Depending on extensive datasets, offer perceptions and predictions by using cloud computing resources which creates and assess predictive analytics frameworks.
• Main Areas: Cloud-based data processing, predictive modeling and machine learning.

5. Data Visualization in the Cloud

• Topic: Cloud-Based Data Visualization Techniques for Big Data
• Explanation: In order to assist the users to read and interpret big data, investigate the dynamic visualization methods which could be employed in the cloud infrastructures.
• Main Areas: User experience model, interactive representations and adaptable visualization environments.

6. AI and Machine Learning Integration

• Topic: Integrating AI and Machine Learning with Cloud-Based Data Analytics
• Explanation: Improve the data-based perspectives through examining the synthesization of AI and machine learning algorithms with cloud-driven data analytics.
• Main Areas: AI-driven data analytics, model training and application and scalable machine learning models. Adherence, access management and encryption are the key focus of this research area.

7. Data Security and Privacy in Cloud Analytics

• Topic: Ensuring Data Security and Privacy in Cloud-Based Data Analytics
• Explanation: Across cloud-based analytics, assure the security and secrecy of data by designing effective techniques.
• Main Areas: Secure data distribution, privacy-preserving analytics and data encryption.

8. Multi-Cloud Data Analytics

• Topic: Implementing Data Analytics Across Multi-Cloud Environments
• Explanation: For the process of executing data analytics approaches which extends diverse cloud providers, conduct a research on involved issues and findings.
• Main Areas: Cross-cloud data processing, compatibility and data synthesization.

9. Cloud-Based Data Warehousing

• Topic: Optimizing Cloud-Based Data Warehousing for Efficient Data Analysis
• Explanation: Assist dynamic and scalable data analysis for cloud-based data warehouses through investigating optimization algorithms.
• Main Areas: ETL processes, query enhancement and data warehousing models.

10. Serverless Data Analytics

• Topic: Leveraging Serverless Architectures for Data Analytics in the Cloud
• Explanation: Regarding data analytics programs, analyze the application of serverless computing that emphasizes the performance, scalability and financial efficiency.
• Main Areas: FaaS (Function-as-a-service), event-driven data processing and serverless models.

11. Edge Computing for Cloud Data Analytics

• Topic: Enhancing Cloud Data Analytics with Edge Computing
• Explanation: This research area highlights enhancing real-time data processing and decreasing response time and considering the computing process of cloud data analytics, it examines the performance of edge computing.
• Main Areas: Data preprocessing at the edge, real-time analytics and cooperation of edge-cloud.

12. Data Governance in Cloud Computing

• Topic: Implementing Data Governance Frameworks for Cloud-Based Data Analytics
• Explanation: In cloud-based data analytics, assure the security, data quality and adherence by creating and assessing data governance models.
• Main Areas: Disciplinary compliance, data lifecycle management and data quality management.

13. Automated Data Analytics Pipelines

• Topic: Designing Automated Data Analytics Pipelines in the Cloud
• Explanation: As a means to facilitate analysis, processing and data consumption, the model and execution of automated data analytics pipelines should be analyzed.
• Main Areas: Data pipeline orchestration, persistent data synthesization and Workflow automation.

14. Cost Optimization in Cloud Data Analytics

• Topic: Cost-Efficient Strategies for Data Analytics in Cloud Computing
• Explanation: By concentrating on resource utilization and consumption guidelines in cloud platforms, enhance the cost of data analytics load densities through examining the efficient policies.
• Main Areas: Cloud pricing frameworks, resource scheduling and financial management.

15. IoT Data Analytics in the Cloud

• Topic: Scalable Data Analytics for IoT Applications Using Cloud Computing
• Explanation: Specifically for IoT applications, use cloud computing resources to investigate the scalability and performance of data analytics solutions.
• Main Areas: Real-time analytics, cloud-IoT integration and IoT data processing.

Instance of  Thesis Topic: Real-Time Data Stream Processing and Analytics in the Cloud

Goals:

• In cloud platforms, accomplish actual-time data processing and analytics by creating and assessing methods.
• For practical applications, it is required to attain minimal latency and high throughput data processing.

Methodology:

1. Literature Analysis:

• Carry out an extensive analysis on current stream processing models and methods.
• In real-time data analytics, detect crucial problems and possibilities.

2. System Model:

• Use cloud services to develop real-time data stream processing systems.
• By encompassing the process, storage elements and data consumption, specify the model.

3. Execution:

• Apply cloud-based stream processing models such as AWS Kinesis, Apache Kafka and Apache Flink to execute the system.
• To operate and evaluate data streams, create real-time analytics techniques.

4. Assessment:

• Based on scalability, response time and throughput, analyze the performance of the system by carrying out an experimental approach.
• In order to establish enhancements, contrast the preferred models with current findings.

5. Case Works:

• For real-world applications like social media data analysis, financial transactions observation and IoT data analytics, implement the model.

Tools &  Mechanisms:        

• Programming Languages: Java, Scala and Python.
• Stream Processing Frameworks: Apache Flink, AWS Kinesis and Apache Kafka.
• Cloud Platforms: Azure, Google Cloud and AWS.
• Data Storage Solutions: GoogleBigQuery, Azure Data Lake and Amazon S3.
• Data Visualization Tools: Kibana, Grafana and Tableau.