As the cloud computing domain evolves rapidly with novel theories, there might be emergence of some specific research issues. Many scholars face a variety of research challenges in Cloud Computing when working on their thesis or implementation. Our team is highly qualified and dedicated to enhancing scholars’ efficiency. To interpret the involved problems explicitly, we provide few considerable challenges on the domain of cloud computing:
- Security and Privacy
Problems:
- Data Violations: From illicit access, it is crucial to secure the sensitive data.
- Encryption: To balance performance and security, creating an effective encryption method is very significant.
- Access Management: For verifying the legal users, whether they are only approaching particular data and functions, execute effective access control techniques.
- Intrusion Detection: In real-time, detect and react to security assaults by developing an efficient IDS (Intrusion Detection Systems).
- Multi-Tenancy: Regarding the multi-tenant cloud platforms, assuring the security and data isolation is a major challenge.
- Resource Management and Optimization
Problems:
- Dynamic Resource utilization: Considering the diverse load densities and requirement patterns create efficient methods for dynamic resource utilization.
- Load Balancing: Across diverse servers, prohibit under exploitation and overburdening by verifying the authentic allocation of load densities.
- Energy Efficiency: As preserving the performance and service-level requirements, it is significant to decrease energy usage in cloud data centers.
- Adaptability: According to the user requirements and evolving data, developed systems must be capable of evaluating in an effortless manner.
- Interoperability and Portability
Problems:
- Normalization: Among various cloud environments, assure interoperability by designing benchmarking protocols and APIs.
- Data Flexibility: Without the considerable spare time and data loss, smooth data migration and flexibility have to be supported among various cloud providers.
- Multi-Cloud Management: Over several cloud providers, it is important to handle resources and functions.
- Performance and Reliability
Problems:
- Response Time: Particularly for applications which need real-time processing, assure the rapid response time by decreasing the latency period.
- Fault Tolerance: As excluding the data loss or important spare time, the developed system should be efficient in defending and recovering from breakdowns.
- QoS (Quality of Service): To address SLAs (Service-Level Agreements), assure integrity and sustained functionality.
- Data Management
Problems:
- Big Data Processing: With the aim of analyzing and evaluating huge volumes of data, it is important to design adaptable and effective techniques.
- Data Consistency: Across shared cloud platforms, preserving the data consistency is very essential.
- Storage Findings: For managing the various data types and sizes, cost-efficient and adaptable storage findings need to be modeled.
- Network Management
Problems:
- Bandwidth Enhancement: To manage high data transfer rates, it intends to handle and enhance network bandwidth in an effective manner.
- Latency Minimization: Especially for enhancing the performance of cloud applications, network response time should be decreased.
- Security: In opposition to manipulation and interruption, it is significant to protect the data throughout the transmission process.
- Edge and Fog Computing
Problems:
- Synthesization: As a means to enhance storage capacities and data processing, accomplishing the effortless synthesization of edge and fog computing with cloud models is the main challenge.
- Response Time and Bandwidth: Regarding the edge and fog computing platforms, response time and bandwidth constraints required to be managed.
- Resource Management: In a shared edge and fog computing platform, resources have to be addressed effectively.
- Economics and Cost Management
Problems:
- Financial Efficiency: While preserving performance and integrity, enhance the cost related to cloud functions through generating tactics.
- Commercial Models: In order to indicate the application and resource usage, development of authentic and obvious pricing models is most important.
- Billing and Tracking: Monitor the resource consumption and costs by executing the exact billing and tracking systems.
- Regulatory and Compliance
Problems:
- Data Integrity: Assuring compliance with data integrity rules is very critical. Across particular spatial limits, these rules need efficient data storage.
- Compliance Certification: It is required to obey diverse compliance regulations and models like PCI DSS, HIPAA and GDPR.
- Verifiability: With the aim of assuring the adherence with authentic and standard demands, offer capable techniques for examining the cloud functions.
- Sustainability and Green Computing
Problems:
- Energy Usage: To decrease the ecological implications, the energy usage of cloud data centers must be diminished.
- Carbon Footprint: Effective methods have to be created for the purpose of evaluating and decreasing the greenhouse gas emission of cloud functions.
- Renewable Energy: The synthesization of sustainable energy sources with cloud data center functions is the key challenge.
- Artificial Intelligence and Machine Learning Integration
Problems:
- Adaptability of AI Frameworks: In cloud platforms, it is crucial to assure AI (Artificial Intelligence) models.
- Resource Utilization for AI Load Densities: For the purpose of training and implementing AI models in the cloud, there is a necessity for dynamic resource allocation.
- Data Secrecy: Specifically while handling the sensible data in cloud computing, assure the secrecy in application of AI (Artificial Intelligence) and ML (machine Learning).
What are some of the research topics in cloud computing?
Encompassing the various areas like edge and fog computing, resource management and optimization, big data analytics, green cloud computing and other significant fields, we suggest some of the captivating research topics on cloud computing:
- Cloud Security and Privacy
- Homomorphic Encryption: Without the need of decoding, conduct evaluations on encrypted data by creating effective homomorphic encryption methods.
- Blockchain for Cloud Security: To improve reliability, clarity and security of cloud data, make use of blockchain mechanisms.
- Intrusion Detection Systems (IDS): In cloud settings, identify and reduce attacks through developing machine learning-oriented IDS.
- Privacy-Preserving Data Mining: On cloud data, carry out data mining while maintaining user secrecy through designing techniques.
- Access Control Mechanisms: Effective access control techniques have to be modeled for cloud platforms like ABAC (Attribute-Based Access Control).
- Resource Management and Optimization
- Dynamic Resource Allocation: In terms of real-time requirements, design techniques for robust and dynamic resource utilization.
- Load Balancing: Across cloud servers, allocate load densities by creating enhanced load balancing algorithms.
- Energy-Efficient Resource Management: Regarding cloud data centers, decrease energy usage while preserving the performance through exploring the techniques.
- Predictive Resource Scaling: Correspondingly forecast resource requirements and evaluate resources with the help of machine learning techniques.
- Container Orchestration: For high-level resource management, the container orchestration tools such as Kubernetes must be improved.
- Performance and Scalability
- Latency Reduction Techniques: To decrease response time for cloud-oriented applications, investigate the sufficient methods.
- High-Performance Computing (HPC) in the Cloud: Especially for better evaluating applications, the application of cloud models is required to be explored.
- Scalable Big Data Processing: In cloud settings, address extensive-scale data processing by creating models and techniques.
- Serverless Computing Optimization: Considering the serverless computing models, the performance and affordability are improved.
- Edge and Fog Computing
- Edge-Fog-Cloud Integration: For best performance, synthesize cloud, edge and fog computing by developing efficient models.
- Resource Management in Edge/Fog Computing: In edge and fog platforms, techniques need to be created for dynamic resource allocation and management.
- Security in Edge/Fog Computing: the security issues and findings in edge and fog computing should be explored extensively.
- Real-Time Data Processing: At the edge and fog nodes, robust algorithms must be designed particularly for real-time data processing.
- Multi-Cloud and Hybrid Cloud
- Multi-Cloud Management: Over diverse cloud providers, handle the resources by generating models.
- Data Portability and Interoperability: Among various cloud environments, crucially verify the effortless data migration and compatibility.
- Cost Optimization in Multi-Cloud: As preserving the performance in multi-cloud settings, reduce the costs by exploring the significant tactics.
- Security and Compliance in Multi-Cloud: In multi-cloud configurations, security and compliance challenges are required to be managed.
- Artificial Intelligence and Machine Learning in the Cloud
- Scalable Machine Learning: By using cloud models, create adaptable machine learning techniques.
- AI-Powered Cloud Management: Anticipate the system breakdowns and enhance cloud resource management with the help of AI (Artificial Intelligence).
- Federated Learning: Across the shared cloud platforms, prepare machine learning frameworks while maintaining data secrecy by executing federated learning techniques.
- Automated Machine Learning (AutoML): Regarding the cloud settings, improve performance and usability through implementing advanced AutoML models.
- Big Data Analytics in the Cloud
- Real-Time Analytics: For real-time big data analytics in cloud platforms, efficient models need to be created.
- Data Lake Management: To address and query data lakes which are presented in the cloud, conduct research in compelling algorithms.
- Predictive Analytics: In diverse fields like logistics, finance and healthcare, make use of cloud resources for predictive analytics applications.
- Data Visualization: By implementing cloud computing capacities, design enhanced data visualization tools.
- Green Cloud Computing
- Energy-Efficient Data Centers: For cloud data centers, energy-efficient models and cooling systems have to be developed.
- Renewable Energy Integration: The application of renewable energy sources to robust cloud data centers are explored thoroughly.
- Carbon Footprint Reduction: Considering the cloud computing functions, evaluate and decrease the carbon footprint by creating tactics.
- Cloud-Native Application Development
- Microservices Architecture: Particularly for high-level integrity, security and performance, Microservices models should be improved.
- DevOps and Continuous Deployment: In cloud-native platforms, optimal approaches need to be considered for DevOps and consistent implementation.
- Resilience Engineering: To tackle breakdowns and interruptions, develop robust cloud-native applications through formulating significant methods.
- Internet of Things (IoT) and Cloud Integration
- IoT Data Management: For the purpose of handling and analyzing huge volumes of IoT data in the cloud, productive models have to be formulated.
- IoT Security: As regards IoT-cloud integrated systems, the involved security issues should be addressed.
- Edge Analytics for IoT: To decrease bandwidth allocation and response time, conduct edge analytics by designing capable techniques.
- IoT Device Management: By using cloud models, optimal paths for addressing and enhancing IoT devices are explored.
- Economics and Cost Management in Cloud Computing
- Cost Prediction Models: Depending on consumption patterns, forecast the price of cloud services through generating frameworks.
- Billing and Chargeback Mechanisms: Regarding cloud services, improve the chargeback and billing technologies.
- Commercial Models: Considering the cloud services, authentic and effective pricing frameworks need to be explored.
- Disaster Recovery and Business Continuity
- Automated Disaster Recovery: For cloud platforms, the automated disaster recovery findings should be created.
- Resilient Architectures: At the event of breakdowns, assure industrial stability by developing models.
- Data Backup Strategies: In cloud platforms, effective data backups and recovery tactics are required to be explored.
Thesis Challenges in Cloud Computing
Scholar encounter numerous challenges when it comes to Thesis in Cloud Computing. At phdtopic.com, our team of skilled researchers is equipped to handle any issue thanks to our vast resources and technical support. Phdtopic.com provide top-notch solutions. Share your details with us for a seamless experience.
- A Honey Bee behaviour inspired novel Attribute-based access control using enhanced Bell-Lapadula model in cloud computing
- MOMCC: Market-oriented architecture for Mobile Cloud Computing based on Service Oriented Architecture
- UbiCloud: A Cloud Computing System for Ubiquitous Terminals Based on End User Virtualization
- Investigation of security challenges and a novel security mechanism for cloud computing environment
- Improvement of Data Throughput in Data-Intensive Cloud Computing Applications
- Semantic Computing, Cloud Computing, and Semantic Search Engine
- Information success model for learning system in cloud computing environment
- Seclogmon : Security in cloud computing using activity log for consumer data protection
- A Self-Adaptive Approach for Managing Applications and Harnessing Renewable Energy for Sustainable Cloud Computing
- Computer guided product EMC compliance on user’s workshop with a smart phone — Cloud computing
- Mobile cloud computing: A survey and propose solution framework
- The use of Cloud Computing and mobile technologies to facilitate access to an e-learning solution in higher education context work in progress
- A Cost-efficient Smart IoT Device Controlling System Based on Bluetooth Mesh and Cloud Computing
- Research on E-government Information Security Based on Cloud Computing
- Sensing as a service: A cloud computing system for mobile phone sensing
- Efficient resource arbitration and allocation strategies in cloud computing through virtualization
- Bio-inspired technique for the Virtual Machine Migration in Green Cloud Computing
- Cloud Computing Resources Utilization and Cost Optimization for Processing Cloud Assets
- Cloudlet-screen computing: A multi-core-based, cloud-computing-oriented, traditional-computing-compatible parallel computing Paradigm for the masses
- Empowering education through mobile cloud computing based learning process models