IEEE Projects For CSE In Cloud Computing

IEEE Projects For CSE In Cloud Computing is an intriguing process that must be carried out by following several procedures. For computer science engineering (CSE) students, the IEEE projects can encompass various topics such as machine learning, IoT, big data, resource management, and cloud security. Suitable for IEEE-based projects in cloud computing, we suggest a few major and compelling plans:

1. Enhanced Data Security in Cloud Computing Using Homomorphic Encryption

• Explanation: To carry out computations on encrypted data excluding the decryption process, a cloud storage framework has to be deployed, which utilizes homomorphic encryption.
• Mechanisms: Homomorphic Encryption Libraries, AWS S3, and Python.
• Expertise Acquired: Cloud Storage, encryption, and data protection.

2. Dynamic Resource Allocation for Cloud Data Centers Using Machine Learning

• Explanation: In a cloud data center, forecast resource requirements and allocate resources in a dynamic manner by creating a machine learning model.
• Mechanisms: AWS, Kubernetes, TensorFlow, and Python.
• Expertise Acquired: Cloud computing, resource handling, and machine learning.

3. IoT Data Analytics on Cloud Using Apache Kafka and Spark

• Explanation: On cloud infrastructure, we plan to build an IoT data pipeline. It is important to utilize Apache Spark for actual-time analytics and Apache Kafka for data ingestion.
• Mechanisms: IoT Devices, AWS, Apache Spark, and Apache Kafka.
• Expertise Acquired: Cloud infrastructure, actual-time data analytics, and IoT.

4. Serverless Function Chaining for Scalable Microservices

• Explanation: For scalable microservices, a chain of functions has to be developed. To accomplish this process, a serverless application must be deployed with AWS Lambda.
• Mechanisms: Node.js, AWS API Gateway, and AWS Lambda.
• Expertise Acquired: Cloud services, microservices, and serverless architecture.

5. Energy-Efficient Virtual Machine Placement in Cloud Data Centers

• Explanation: In cloud data centers, accomplish energy-effective deployment of virtual machines by creating and applying an algorithm.
• Mechanisms: Python, Java, and CloudSim.
• Expertise Acquired: Cloud Simulation, VM deployment, and energy effectiveness.

6. Blockchain-Based Secure Data Sharing in Cloud Environments

• Explanation: For reliable and safer transmission of data in cloud platforms, we intend to develop a blockchain-related framework.
• Mechanisms: Node.js, AWS, and Hyperledger Fabric.
• Expertise Acquired: Cloud computing, data protection, and blockchain.

7. Cost Optimization in Multi-Cloud Environments Using Genetic Algorithms

• Explanation: In multi-cloud platforms, the cost of active applications should be improved by creating a genetic algorithm.
• Mechanisms: Azure, Google Cloud, AWS, and Python.
• Expertise Acquired: Genetic algorithms, multi-cloud handling, and cost optimization.

8. Implementing Quality of Service (QoS) in Cloud-Based Video Streaming

• Explanation: A cloud-related video streaming service has to be developed, which utilizes resource allocation and load balancing methods to assure quality of service.
• Mechanisms: Node.js, Python, and AWS Media Services.
• Expertise Acquired: Load balancing, video streaming, and QoS.

9. Disaster Recovery Planning in Cloud Computing Using Data Replication Techniques

• Explanation: To assure data availability, we aim to apply a disaster recovery strategy with data replication methods, especially for cloud applications.
• Mechanisms: Python, AWS RDS, and AWS S3.
• Expertise Acquired: Cloud infrastructure, data replication, and disaster recovery.

10. Developing a Cloud-Based Health Monitoring System for Remote Patients

• Explanation: A cloud-related health monitoring framework should be developed, which offers actual-time analytics and notifications by gathering data from wearable devices.
• Mechanisms: React, Python, AWS Lambda, and AWS IoT Core.
• Expertise Acquired: Actual-time analytics, IoT, and health tracking.

Research Methodology

1. Introduction:

• Research Background: Regarding the selected topic, we should offer background and setting.
• Problem Statement: In an explicit manner, the issue has to be specified, which our research intends to resolve.
• Goals: Our project goals have to be summarized clearly.
• Research Queries: The major research queries should be designed.
• Scope and Shortcomings: Any shortcomings and scope must be specified.

2. Literature Survey:

• Outline: Relevant to our project topic, a survey of current literature has to be carried out.
• Theoretical Framework: Important concepts and models must be described.
• Gap Analysis: In the literature, we have to find gaps that can be fulfilled by our project.
• Relevant Work: Relevant to our topic, major studies have to be outlined.

3. Research Design:

• Research Methodology: It is crucial to determine our research methodology. It could be quantitative, qualitative, or an integration of both.
• Research Type: Focus on specifying the research type. Some of the potential types are evaluative, explanatory, descriptive, and exploratory.
• Hypotheses: If our study is hypothesis-based, we need to design hypotheses.

4. Methodology:

• Data Gathering:
• Primary Data: The process of gathering primary data has to be explained (for instance: surveys, experiments).
• Secondary Data: Secondary data sources should be detailed (such as current datasets).
• Tools and Mechanisms: It is significant to define the suitable environments, mechanisms, and tools.
• Experimental Configuration: The system and arrangement must be described.
• Data Analysis:
• Methods: The data analysis methods have to be detailed.
• Software: For data analysis, the ideal software tools should be specified.
• Validation: The process of verifying our outcomes has to be explained.

5. Implementation:

• Development Procedure: In the execution, the conducted procedures must be explained.
• Algorithms and Models: Any frameworks, models, or algorithms have to be described, which we utilize or create in our study.
• Technical Issues: Potential technical problems should be considered. Plan to describe the process of solving them.

6. Outcomes and Discussion:

• Outcomes Presentation: By means of tables, graphs, and charts, we should depict our discoveries.
• Analysis: In the background of our research queries, the outcomes must be examined.
• Comparison: With current research or benchmarks, our outcomes have to be compared.
• Discussion: It is significant to address the impacts of our outcomes.

7. Conclusion:

• Outline: The major discoveries have to be outlined.
• Contributions: Focus on our project and emphasize its contributions.
• Upcoming Work: For further study, we have to recommend potential areas.

8. References:

• Citations: By complying with a constant citation style (for instance: APA, IEEE), all the utilized sources and references must be indicated.

Why is cloud security such a big challenge?

In current years, accomplishing and maintaining cloud security are major issues, which require efficient techniques and solutions. Regarding the challenging aspects of cloud security, we offer several major justifications in an explicit manner:

1. Shared Responsibility Model

• Complex Division of Responsibilities: Among the consumer and the cloud service provider (CSP), the security liabilities are shared in cloud computing. In security assurance, misperception and gaps can be resulted through this division.
• Customer and Provider Security Overlaps: When depending on the provider for framework security, the data and applications have to be protected, which should be assured by consumers.

2. Multi-Tenancy

• Resource Sharing: The similar physical resources are shared by several consumers. In the case of inappropriate isolation, the risks of illicit access or data leakage can be caused.

• Isolation Concerns: As potential risks can cause cross-tenant assaults, it is important to assure robust isolation among tenants. However, it is an intricate process.

3. Data Security and Privacy

• Data Breaches: Major reputational and financial impairments can be caused by means of illicit access to confidential data.
• Data Encryption: At the time of processing, transmission, and inactive states, it is difficult to assure data encryption and handle encryption keys, even though encryption is important.
• Data Sovereignty: Diverse data security rules are presented in various jurisdictions. When storing the data across several areas, the compliance can be intricate.

4. Access Control and Identity Management

• Complex Access Management: Specifically in extensive firms including several services and users, it is difficult to handle what resources can be accessed by whom.
• Identity and Access Management (IAM): It is significant to assure the preparedness of robust authentication, accounting approaches, and authorization. However, constantly applying them can be complicated.

5. Compliance and Regulatory Requirements

• Diverse Regulations: Several important rules like PCI-DSS, HIPAA, and GDPR should be followed by firms. Based on areas, these rules can differ in a substantial manner.
• Auditing and Reporting: In dynamic cloud platforms, it is intricate to review and report on security techniques. Additionally, assuring constant compliance is also difficult.

6. Dynamic and Elastic Nature of the Cloud

• Resource Scaling: The use of constant security strategies and controls can be complex, especially in the case of dynamic scaling of resources.
• Ephemeral Instances: Carrying out forensic analysis and preserving security records are challenging due to the volatile nature of cloud instances.

7. Advanced Threats and Attack Vectors

• Sophisticated Cyber Attacks: Several complex cyber assaults like DDoS assaults, advanced persistent threats (APTs), and others are mostly focused on cloud platforms.
• Surface Area of Attack: The possible attack area is expanded through the distributed and extensive nature of cloud services. Protecting all access points can be difficult due to this issue.

8. Insider Threats

• Malicious Insiders: Major security risks can be caused by third-party contractors or workers who have access to important frameworks.
• Unintentional Mistakes: Security violations can be resulted through human faults, like unintentional data revelation or mis-arrangements.

9. Service Availability and Business Continuity

• Downtime and Disruptions: In opposition to DDoS assaults and other interruptions, it is important to assure robustness and high availability.
• Disaster Recovery: To manage advanced cloud architectures, it is intricate to apply and preserve efficient disaster recovery strategies.

10. Lack of Visibility and Control

• Limited Control: Across the fundamental framework, limited control is presented to customers. They should depend on the security techniques of CSP.
• Visibility Challenges: In all resources and events in the cloud, acquiring extensive visibility can be challenging. It can also obstruct detection of hazards and response endeavors.

11. Integration with Existing Systems

• Hybrid Environments: Among hybrid platforms, assuring constant security is difficult. With on-sites frameworks, combining cloud services is also challenging.
• Legacy Systems: Supplementary issues can be caused in the case of protecting legacy frameworks, specifically when they were not modeled for cloud incorporation.

Relevant to the domain of cloud computing, we listed out several interesting plans for IEEE projects, along with concise explanations, mechanisms, and acquired expertise. By considering the problems in cloud security, numerous important reasons are provided by us.

IEEE Projects For CSE In Cloud Computing Topics for Research

IEEE Projects For CSE In Cloud Computing Topics for Research are provided by us contact us , we provide project guidance for all scholars get your work done  under one roof. We will your stepping stone for your reasech career.

1. Coordinated predictive control of networked multiagent systems via distributed cloud computing with time-varying transmission delays
2. Energy efficient fault tolerance techniques in green cloud computing: A systematic survey and taxonomy
3. Virtual cloud computing–based and 3D multi-physics simulation for local oxygen starvation in PEM fuel cell
4. Evaluation of next-generation high-order compressible fluid dynamic solver on cloud computing for complex industrial flows
5. A review on job scheduling technique in cloud computing and priority rule based intelligent framework
6. SWEP-RF: Accuracy sliding window-based ensemble pruning method for latent sector error prediction in cloud storage computing
7. Identifying the role of cloud computing technology in management of educational institutions
8. An energy-efficient and secure identity based RFID authentication scheme for vehicular cloud computing
9. VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm
10. A new fuzzy-based method for energy-aware resource allocation in vehicular cloud computing using a nature-inspired algorithm
11. A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning
12. A novel rate control algorithm for low latency video coding base on mobile edge cloud computing
13. Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms
14. Modelling of smart risk assessment approach for cloud computing environment using AI & supervised machine learning algorithms
15. Environmental impacts of earth observation data in the constellation and cloud computing era
16. Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network
17. HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment
18. Fuzzy correlational analysis for dynamic consolidation of virtual machines in cloud computing environment
19. Improvement of tasks scheduling algorithm based on load balancing candidate method under cloud computing environment
20. Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy