Cloud Computing Topics for Research Paper

There are several research gaps exist in the field of cloud computing. Together with detected research gaps, we provide few research topics in cloud computing that contains the capability to create the foundation for a captivating research paper:

Security and Privacy in Multi-Cloud Environments

Research Gaps:
For assuring data confidentiality among various cloud suppliers, the standardized models are insufficient.
The study based on safe data migration approaches among clouds are inadequate.
In multi-cloud platforms, there is insufficient research on actual-time attack identification and response technologies.

Resource Management and Optimization

Research Gaps:
To adjust to varying workloads in a dynamic manner and improve resource allotment in actual-time, there is a requirement for progressive methods.
In order to stabilize effectiveness and expense, research of energy-effective resource management policies is inadequate.
The study based on the influence of heterogeneous sources on entire cloud effectiveness are insufficient. Exploration on how to handle them in an efficient manner is also inadequate.

Serverless Computing

Research Gaps:
In serverless platforms, extensive studies on improving cold start latency is inadequate.
Generally, for extensive applications, there is insufficient interpretation of the cost-effectiveness trade-offs in serverless computing.
Specific to serverless infrastructures, study on safety limitations, like function-level risks are limited.

Edge Computing and IoT Integration

Research Gaps:
To assure consistent combination and data synchronization among edge devices and cloud environments, effective models are required.
Research based on safety impacts of implementing IoT applications on the edge are insufficient.
Investigation on the basis of actual-time analytics abilities and their influence on effectiveness and delay in the platforms of edge computing are limited.

AI and Machine Learning in Cloud Management

Research Gaps:
Typically, AI-based resource management frameworks are required that are capable of forecasting workload trends and improving resource allotment pre-emptively.
For predictive maintenance, there is lack of exploration regarding the combination of machine learning frameworks with cloud tracking tools.
Research based on the moral impacts and unfairness in AI frameworks employed for cloud management are inadequate.

Blockchain for Cloud Security

Research Gaps:
To manage the more transaction throughput needed for cloud platforms, there is a requirement for scalable blockchain infrastructures.
For safe multi-party computation in cloud scenarios, the research of the efficiency of blockchain is insufficient.
Typically, there is inadequate study based on the effectiveness and expense impacts of combining blockchain with cloud services.

Cloud-Native Application Development

Research Gaps:
For creating and implementing cloud-native applications that improve consistency and scalability, there is a necessity for methodologies and effective approaches.
On the basis of resource consumption and application effectiveness, investigations on the influence of container orchestration tools such as Kubernetes are inadequate.
Study based on the safety limitations that are produced by microservices infrastructures and ways to reduce them are needed.

Disaster Recovery and Business Continuity

Research Gaps:
To decrease interruption and loss of data in the platforms of cloud, there is insufficiency of progressive, automated disaster recovery approaches.
Exploration based on cost-efficient policies for cross-region disaster recovery and failover are inadequate.
For predictive disaster recovery scheduling, research on the combination of machine learning and AI are limited.

Quantum Computing in the Cloud

Research Gaps:
To enable the combination of quantum computing with conventional cloud services, there is a requirement for suitable models.
In the platforms of cloud, there is insufficient exploration of realistic application areas and implementations of quantum computing.
The study on the basis of confidentiality and protection impacts of cloud computing in the cloud are inadequate.

Performance Benchmarking and Optimization

Research Gaps:
For contrasting the effectiveness of various cloud services and suppliers, standardized benchmarks are necessary.
Investigations on the influence of containerization and virtualization on cloud effectiveness are insufficient.
For improving the effectiveness of cloud-related applications under differing workload situations, study on the basis of effective approaches are limited.

Compliance and Data Governance

Research Gaps:
To assure adherence to numerous regulatory standards in multi-cloud platforms, there is a requirement for extensive models.
Research based on automatic compliance auditing tools which can function among various cloud environments are insufficient.
Generally, studies based on the influence of evolving security rules such as CCPA, GDPR, on cloud service consumers and suppliers are inadequate.

Hybrid Cloud and Multi-Cloud Strategies

Research Gaps:
To offer consistent combination and arrangement among hybrid and multi-cloud platforms, effective management tools are required.
In terms of cost-benefit analysis of implementing hybrid and multi-cloud policies for various kinds of companies, the efficient investigation is insufficient.
Exploration on the basis of the safety limitations and approaches for data management in hybrid and multi-cloud arrangements are inadequate.

What are the Research issues in cloud computing?

In the domain of cloud computing, there are numerous research issues emerging continuously. We offer few significant research problems relevant to cloud computing:

Security and Privacy

Data Breaches and Unauthorized Access

Problem: In a multi-tenant platform where numerous users distribute the similar physical architecture, the process of assuring data protection is determined as a significant issue.
Research Area: Constructing progressive encryption approaches, strong access control technologies, and safe multi-tenancy frameworks.

Data Privacy

Problem: The main challenge is the way of securing confidential data from illicit access and assuring adherence to data safety rules.
Research Area: Differential privacy, homomorphic encryption, and safe data sharing protocols.

Secure Data Migration

Problem: It is difficult to migrate data from on-premises models to the cloud or among cloud suppliers in a secure manner.
Research Area: To assure data privacy and integrity at the time of transmission, create safe migration protocols and models.

Resource Management and Optimization

Dynamic Resource Allocation

Problem: By means of differing and unanticipated workloads, allotting sources to applications in an effective manner is examined as the main challenge.
Research Area: Actual-time resource allocation policies, machine learning-related predictive analytics, and dynamic scaling methods.

Energy Efficiency

Problem: At the time of sustaining consistency and effectiveness, the process of decreasing the energy utilization of cloud data centers is determined as problematic.
Research Area: Renewable energy combination, energy-effective resource management methods, green computing practices.

Heterogeneous Resource Management

Problem: In platforms of cloud, handling and enhancing the utilization of heterogeneous sources such as FPGAs, CPUs, GPUs.
Research Area: For interpreting various abilities and performance features of different sources, explore effective resource scheduling methods.

Performance and Scalability

Performance Isolation

Problem: The major challenge is to assure that the effectiveness of one tenant’s applications does not majorly impact others in a multi-tenant platform.
Research Area: For performance segregation and quality of service (QoS) assurances, construct suitable approaches.

Scalability

Problem: The procedure of assuring that the cloud models are able to scale in an effective manner to manage huge datasets and enhancing loads is considered as a major issue.
Research Area: Flexible scaling technologies, scalable cloud infrastructures, and distributed computing models.

Network Management

Latency Reduction

Problem: Specifically, for actual-time and latency-sensitive applications, the way of decreasing network delay to enhance the effectiveness of cloud applications are problematic.
Research Area: Improved network protocols, edge computing, and content delivery networks (CDNs).

Bandwidth Management

Problem: To manage huge amounts of data transfer among cloud data centers and end-users, the way of handling bandwidth in an effective manner is determined as a key challenge.
Research Area: Software-defined networking (SDN), bandwidth improvement approaches, and traffic engineering.

Interoperability and Portability

Multi-Cloud Interoperability

Problem: Among various cloud suppliers, assuring consistent combination and interoperability is the significant issue.
Research Area: Cloud service brokers, standardized APIs, and multi-cloud management environments.

Data and Application Portability

Problem: Without substantial modification, enabling the movement of data and application among various cloud platforms.
Research Area: Cross-cloud migration tools, containerization mechanisms such as Kubernetes, Docker, and cloud-native application creation.

Edge and Fog Computing

Integration with Cloud Computing

Problem: In order to manage data processing nearer to the data source, combining fog and edge computing with conventional cloud computing in a consistent way is the key challenge.
Research Area: Data synchronization approaches, hybrid cloud infrastructures, and edge-to-cloud continuum frameworks.

Security and Privacy in Edge Computing

Problem: The major difficulty is assuring confidentiality and protection in platforms of edge computing where data is processed nearer to the end-users.
Research Area: Confidentiality-preserving data processing, lightweight encryption approaches, and safe edge device management.

Disaster Recovery and Business Continuity

Automated Disaster Recovery

Problem: To reduce interruption and loss of data, constructing automatic and effective disaster recovery technologies are examined as the main issue.
Research Area: Distributed storage models, actual-time replication, and automatic failover technologies.

Cost-Effective Business Continuity

Problem: The significant challenge is assuring business consistency without experiencing higher expenses.
Research Area: Hybrid disaster recovery frameworks, cost-benefit analysis of disaster recovery approaches, and cloud-related backup policies.

Compliance and Data Governance

Regulatory Compliance

Problem: The way of assuring that the cloud services adhere to different regulatory necessities such as PCI-DSS, GDPR, HIPAA, are examined as the main challenge.
Research Area: Policy-based data management, automatic compliance auditing tools, and confidentiality-preserving data analytics.

Data Governance

Problem: The major issue is the process of handling the lifecycle of data and assuring data integrity, protection, and accessibility all over its lifecycle.
Research Area: Data lifecycle management tools, data governance models, and metadata management.

AI and Machine Learning in Cloud Computing

AI-Driven Cloud Management

Problem: To improve resource management and cloud functions, utilizing machine learning and AI are examined as a main concern.
Research Area: Predictive analytics for workload management, anomaly identification, and automated resource scaling.

Machine Learning Model Deployment

Problem: In platforms of cloud, implementing and handling machine learning frameworks in an effective manner is determined as a significant challenge.
Research Area: Scalable implementation policies, system lifecycle management, and distributed training and interpretation.

Blockchain Integration with Cloud Computing

Blockchain for Security

Problem: In cloud computing, employing blockchain to improve belief and protection.
Research Area: Safe multi-party computation, decentralized access control, and data integrity verification.

Performance and Scalability of Blockchain

Problem: The significant concern is the way of assuring the scalability and effectiveness of blockchain when combined with cloud services.
Research Area: Performance enhancement approaches, scalable blockchain infrastructures, and consensus methods.

Cloud Computing Topics for Research Paper

The experts at phdprojects.org demonstrate exceptional talent in developing research paper topics related to Cloud Computing. Through the provision of innovative and unique topics, we have established ourselves as global leaders in research expertise. We offer both online and offline support to scholars, ensuring that your Cloud Computing paper is meticulously crafted in accordance with university standards.

A trust computing mechanism for cloud computing with multilevel thresholding
Secure framework for data access using Location based service in Mobile Cloud Computing
Parameter Identification of IoT-Embedded PMSMs Using an AWS Cloud-Computing Platform
Load balancing in cloud computing using dynamic load management algorithm
Secure Data in Cloud Computing Using Face Detection and Fingerprint
Using Heterogeneous Cloud Computing to Manage Resources in Sustainable Cyber-Physical Systems
Classification of Data to Enhance Data Security in Cloud Computing
Performance evaluation and improvement in cloud computing environment
Sub-millisecond level latency sensitive Cloud Computing infrastructure
Critical path based scheduling algorithm for workflow applications in cloud computing
A pareto-based Artificial Bee Colony and product line for optimizing scheduling of VM on cloud computing
CLOUDIO: A Cloud Computing-Oriented Multi-tenant Architecture for Business Information Systems
Study of Multistage Anomaly Detection for Secured Cloud Computing Resources in Future Internet
Approach to the effective controlling cloud computing resources in data centers for providing multimedia services
Alternatives to cloud computing for building a unified-data computing system
Applying Hadoop’s MapReduce framework on clustering the GPS signals through cloud computing
A Proposed Software Protection Mechanism for Autonomous Vehicular Cloud Computing
A Serverless Cloud Computing Framework for Real-Time Appliance-Usage Recommendation
Thermal Management and Load Control of Container Data Center: A Case Study of Cloud Computing in a Rack
Priority based task scheduling by mapping conflict-free resources and Optimized workload utilization in cloud computing