Latest Topics In Cloud Computing with detailed explanation are provided by us. You can reach cloudcomputingprojects.net we guide you on all level if you are stuck up in-between, we will guide you. Related to cloud computing, we suggest some interesting topics that support the advancement of the domain and offer important perceptions. Talk to our domain experts directly we tackle all your hurdles into published success. To investigate and assess various aspects of cloud computing, these topics enable students.
- Comparative Analysis of Cloud Service Providers
Explanation: For major cloud service providers (Microsoft Azure, Google Cloud, AWS), an extensive comparative analysis must be carried out. It is important to consider service contributions, security, cost, and functionality.
Goals:
- The functionality of services such as networking, storage, and compute should be compared.
- Pricing models and cost structures have to be examined.
- Compliance certifications and security characteristics must be assessed.
- Service-level agreements (SLAs) and customer support have to be evaluated.
- Performance Comparison of Virtualization Techniques in Cloud Computing
Explanation: For cloud platforms, we focus on various virtualization methods (VMware, Xen, and KVM) and compare their functionality.
Goals:
- On I/O functionality, memory, and CPU, the effect of each virtualization method has to be assessed.
- For every virtualization technique, the cost introduced must be studied.
- Resource utilization effectiveness and scalability should be compared.
- Comparative Analysis of Cloud Storage Solutions
Explanation: For cloud storage solutions, a comparative study has to be performed (Azure Blob Storage, Google Cloud Storage, Amazon S3). Concentrate on data durability, cost, and functionality.
Goals:
- Latency and data transfer speeds have to be assessed and compared.
- The cost-efficiency of various pricing models and storage classes should be examined.
- Data durability and availability assurances must be assessed.
- Security Mechanisms in Cloud Computing: A Comparative Study
Explanation: By focusing on hazard detection, identity and access management, and data encryption, different security technologies have to be compared, which are applied by various cloud providers.
Goals:
- Key handling practices and encryption algorithms must be compared.
- Among providers, identity and access management solutions have to be assessed.
- Focus on hazard detection and prevention tools, and examine their efficiency.
- Energy Efficiency in Cloud Data Centers: Comparative Analysis
Explanation: Consider various cloud data centers and compare their energy effectiveness. To minimize energy usage, methods they employ must be compared.
Goals:
- Energy-saving methods must be assessed. It could encompass server consolidation and dynamic voltage and frequency scaling (DVFS).
- Data center infrastructure efficiency (DCIE) metrics and power usage effectiveness (PUE) should be compared.
- Consider cooling and power handling policies, and examine their effect.
- Comparative Study of Auto-Scaling Techniques in Cloud Computing
Explanation: For cloud platforms, various auto-scaling methods and strategies have to be compared, which are utilized to manage diverse workloads.
Goals:
- The functionality and responsiveness of predictive and reactive auto-scaling techniques should be assessed.
- Emphasize on various auto-scaling policies, and examine their cost impacts.
- On application availability and functionality, the effect has to be evaluated.
- Serverless Computing Platforms: A Comparative Analysis
Explanation: For serverless computing environments, a comparative analysis must be performed (Azure Functions, Google Cloud Functions, AWS Lambda). It is crucial to consider scalability, cost, and functionality.
Goals:
- Scalability, cold start latency, and function execution time has to be evaluated.
- Cost effectiveness and pricing models have to be compared.
- Simplicity of placement, management, and combination should be assessed.
- Comparative Analysis of Cloud-Based Big Data Processing Frameworks
Explanation: Based on scalability, functionality, and usability, cloud-related big data processing frameworks must be compared (Flink, Spark, Hadoop).
Goals:
- For different big data workloads, the functionality of each framework has to be evaluated.
- The resource management abilities and scalability should be studied.
- Comfort of maintenance, configuration, and placement must be compared.
- Comparative Study of Hybrid Cloud Management Platforms
Explanation: By focusing on management abilities, functionality, and combination, we plan to compare hybrid cloud management environments (For instance: Microsoft Azure Stack, OpenStack, VMware Cloud Foundation).
Goals:
- Combination with public clouds and on-premises infrastructure should be assessed.
- Resource usage and functionality has to be compared.
- Usability and management characteristics must be evaluated.
- Comparative Analysis of Cloud-Based Disaster Recovery Solutions
Explanation: In terms of cost, recovery point objectives (RPO), and recovery time objectives (RTO), various cloud-related disaster recovery approaches have to be compared (Google Cloud Disaster Recovery, Azure Site Recovery, AWS Disaster Recovery).
Goals:
- For every solution, RTO and RPO metrics must be evaluated and compared.
- Focus on various disaster recovery policies, and examine their cost-efficiency.
- For disaster recovery ideas, the simplicity of configuration and management has to be assessed.
What is the use of a hashing algorithm in a cloud storage service?
A hashing algorithm is utilized in an extensive manner across various areas of cloud computing. In cloud storage services, we specify the major applications of hashing algorithms:
- Data Integrity and Verification
Objective: At the time of storage or transmission, it is important to assure that data has not been altered.
How it Functions:
- By utilizing a hashing algorithm (instance: MD5, SHA-256), a hash value (a fixed-size string of characters) is calculated for the data, while the data is uploaded to the cloud.
- Recalculate and compare the hash value to the actual hash value that is stored with the data, while the data is obtained or downloaded.
- The data integrity is assured, when the hash values are aligned with the actual value. The data has been altered or corrupted, if the hash values are varied.
- Efficient Data Retrieval and Deduplication
Objective: By removing duplicate copies of data, the retrieval effectiveness has to be enhanced and storage costs have to be minimized.
How it Functions:
- Identify duplicate blocks of data or files by employing hashing. Consider the hashing of every block or file. As a unique identifier, the final hash value is employed.
- If the novel data’s hash value aligns with a current hash value, the system ignores storing it again by identifying it as a duplicate. Through this process, storage space can be saved.
- Data Security and Confidentiality
Objective: Sensitive data has to be secured, and the possibility of discovering illegal changes should be assured.
How it Functions:
- To offer data security, hashing is employed along with encryption. By making data illegible to illegal users, the encryption secures data. The potential of identifying illegal alteration of data can be assured by hashing.
- As a means to validate the morality of the encrypted data, the hash values (generally known as message digests) can be utilized, especially when employing cryptographic protocols.
- Efficient Indexing and Searching
Objective: Across extensive datasets, the effectiveness and speed of data indexing and searching has to be enhanced.
How it Functions:
- To enable for rapid explorations of data, hash tables can be generated by hashing algorithms. Consider the hashing of every piece of data. In a hash table, the hash value is utilized as an index.
- Instead of scanning through the whole dataset, this technique searches for the hash value to facilitate fast recovery of data.
- Secure File Uploads and Downloads
Objective: In the cloud, assure that files have not been manipulated, which are uploaded or downloaded.
How it Functions:
- The hash value of the file is calculated by the cloud storage service, when a user uploads a file. It also stores the hash value along with the file.
- The hash can be recomputed by the cloud service, while downloading. At the time of transmission or storage, check that the file has not been changed by comparing the recomputed hash with the stored hash.
- Password Storage and Authentication
Objective: User passwords have to be stored and verified in a safer manner.
How it Functions:
- The hashed value of passwords is stored by cloud services, rather than storing passwords in plain text. The entered password is hashed, when a user logs in. To the stored hash, the final hash is compared.
- The actual passwords remain secure even if the hash values are revealed. From the hash, the process of extracting the actual password is difficult.
- Blockchain and Distributed Ledger Technologies
Objective: In blockchain applications presented in the cloud, the data morality and security has to be assured.
How it Functions:
- For blockchain mechanisms, hashing is more important. For developing a secure chain of blocks, the hash of the previous block is included in every block.
- The following block would be cancelled in the case of any modification in one block. The morality of the whole ledger can be secured.
Sample Application Area: Executing Data Deduplication
For data deduplication in a cloud storage service, hashing has to be utilized. To carry out this process, we offer a basic instance:
- Data Upload:
- To the cloud storage service, a file has to be uploaded by the user.
- The hash value of the file can be calculated by cloud service (for instance: employing SHA-256).
- Verify for Duplicates:
- In the database, the cloud service verifies whether the calculated hash value presents previously.
- The file is considered as a duplicate when the hash value already presents. To the current file, the service stores a reference alone.
- The file is stored, when the hash value is not present. In the database, the hash value is appended.
- Data Retrieval:
- By means of stored hash value, the service retrieves the file, while the user demands the file. Through this approach, effective and rapid access can be assured.
On the basis of cloud computing, several topics are listed out by us, along with explanations and goals. Regarding the applications of a hashing algorithm in a cloud storage service, we offered some justifications.
Latest Thesis Topics in Cloud Computing
Latest Thesis Topics in Cloud Computing that suits your research are listed by us. On all areas we have more than 50+ cloud computing experts, get your work done from our professionals at affordable cost.
- A dynamic conjunctive keywords searchable symmetric encryption scheme for multiple users in cloud computing
- Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing
- Multi-scale change monitoring of water environment using cloud computing in optimal resolution remote sensing images
- Energy Efficient Data Migration Concerning Interoperability Using Optimized Deep Learning in Container-Based Heterogeneous Cloud Computing
- Carbon emission reduction analysis for cloud computing industry: Can carbon emissions trading and technology innovation help?
- An online bi-objective scheduling algorithm for service provisioning in cloud computing
- Designing attribute-based verifiable data storage and retrieval scheme in cloud computing environment
- A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing
- Data-driven internet of things and cloud computing enabled hydropower plant monitoring system
- Application of cloud computing in banking and e-commerce and related security threats
- Estimating the impact of cloud computing on firm performance: An empirical investigation of listed firms
- Evaluation and analysis of forest carbon sequestration and oxygen release value under cloud computing framework
- Risks in Cloud Computing Relationships: A Study of Large Public Buying Organizations in Sweden
- A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing
- Analysing the importance and impact of cloud computing on organization’s performance management during economic crises
- Comparing the impact of Internet of Things and cloud computing on organisational behavior: A survey
- An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework
- Method for evaluation on energy consumption of cloud computing data center based on deep reinforcement learning
- A new offloading method in the green mobile cloud computing based on a hybrid meta-heuristic algorithm
- Optimal cluster based feature selection for intrusion detection system in web and cloud computing environment using hybrid teacher learning optimization enables deep recurrent neural network
