Cloud Computing Thesis Topics

Cloud computing simulation is specifically carried out for examining the efficiency and performance of various aspects in cloud platforms. Relevant to cloud computing simulation frameworks, we suggest a few thesis topics, more than 8000+ scholars are benefited by our services so have a look at the ideas shared which are considered as compelling as well as significant:

  1. Performance Modeling and Simulation of Cloud Data Centers
  • Aim: On the basis of various workloads, the performance of cloud data centers has to be examined. For that, create a simulation framework.
  • Major Factors: Fault tolerance techniques, load balancing, and resource allocation.
  • Tools: iCanCloud, GreenCloud, and CloudSim.
  1. Energy-Efficient Resource Management in Cloud Computing
  • Aim: In cloud data centers, assess energy-saving approaches by developing a simulation framework.
  • Major Factors: Energy-aware scheduling, server consolidation, and Dynamic voltage and frequency scaling (DVFS).
  • Tools: GreenCloud and CloudSim.
  1. Simulation of Multi-Cloud Environments
  • Aim: Among several cloud providers, the implementation and handling of applications have to be designed and simulated.
  • Major Factors: Multi-cloud resource handling, data migration, and interoperability.
  • Tools: MultiCloudSim and CloudSim.
  1. Network Performance Simulation in Cloud Computing
  • Aim: Plan to analyze network performance in cloud platforms by creating a simulation framework.
  • Major Factors: Network congestion, bandwidth usage, and latency.
  • Tools: NetworkCloudSim and CloudSim.
  1. Security and Privacy Simulation in Cloud Environments
  • Aim: In cloud computing, the security techniques must be simulated and assessed.
  • Major Factors: Access control, data encryption, and intrusion identification.
  • Tools: Security-aware CloudSim (SCloudSim) and CloudSim.
  1. Simulation of Serverless Architectures
  • Aim: To examine cost and performance, the serverless computing platforms should be designed and simulated.
  • Major Factors: Resource scaling, function execution delay, and cold starts.
  • Tools: CloudSim and ServerlessSim.
  1. IoT and Cloud Integration Simulation
  • Aim: The combination of IoT devices with cloud computing has to be analyzed through the creation of a simulation model.
  • Major Factors: Scalability, processing latency, and data gathering.
  • Tools: CloudSim and iFogSim.
  1. Simulation of Cloud-Based Big Data Analytics
  • Aim: For examining cost and performance, the big data processing architectures in the cloud platform have to be simulated.
  • Major Factors: Resource usage, job scheduling, and data separation.
  • Tools: Apache Spark, Apache Hadoop, and CloudSim.
  1. Simulation of Fog and Edge Computing in Cloud Environments
  • Aim: The edge and fog computing models must be designed and simulated, which are combined along with cloud computing.
  • Major Factors: Resource sharing, data offloading, and latency minimization.
  • Tools: EdgeCloudSim and iFogSim.
  1. Cost Optimization Strategies in Cloud Computing
  • Aim: Specifically in cloud placements, assess cost-saving policies by creating a simulation framework.
  • Major Factors: Auto-scaling, spot instances, and reserved instances.
  • Tools: CloudCostSim and CloudSim.
  1. Dynamic Resource Allocation and Scheduling in Cloud Computing
  • Aim: In cloud platforms, the dynamic resource allocation and scheduling methods should be simulated.
  • Major Factors: SLA compliance, resource allocation, and task scheduling.
  • Tools: WorkflowSim and CloudSim.
  1. Simulation of Disaster Recovery Mechanisms in Cloud Computing
  • Aim: Various disaster recovery policies in cloud platforms have to be designed and simulated.
  • Major Factors: Recovery time objectives (RTO), failover techniques, and data replication.
  • Tools: DRSim and CloudSim.
  1. Modeling and Simulation of Blockchain in Cloud Computing
  • Aim: The combination of blockchain mechanisms into cloud services has to be simulated.
  • Major Factors: Security, transaction processing, and decentralized storage.
  • Tools: CloudSim and BlockSim.
  1. Simulation of AI Workloads in Cloud Environments
  • Aim: In cloud settings, focus on designing and simulating the AI workloads implementation.
  • Major Factors: Cost-effectiveness, model training time, and GPU usage.
  • Tools: PyTorch, TensorFlow, and CloudSim.
  1. Simulation of Cloud Gaming Platforms
  • Aim: As a means to analyze the performance of cloud gaming environments, create a simulation framework.
  • Major Factors: User experience, frame rate, and latency.
  • Tools: GamingAnywhere and CloudSim.

What are the Research problems in cloud computing?

In the domain of cloud computing, several research issues exist which are required to be solved to fulfill numerous requirements. Regarding this domain, we list out various major research issues, along with potential challenges:

  1. Security and Privacy

Data Security

  • Issue: In the cloud platform, focus on assuring the data accessibility, morality, and privacy.
  • Potential Challenges: Plan to create intrusion detection systems, secure data access techniques, and innovative encryption approaches.

Privacy Preservation

  • Issue: While utilizing cloud services, securing user confidentiality is important.
  • Potential Challenges: Assure adherence to regulations such as GDPR, and apply machine learning approaches and privacy-preserving data mining.

Multi-Tenancy Security

  • Issue: In a multi-tenant platform, assuring safety and isolation is crucial.
  • Potential Challenges: Aim to assure tenant isolation, and obstruct data leakage and side-channel assaults.
  1. Resource Management

Dynamic Resource Allocation

  • Issue: To align with varying requirements, concentrate on allocating resources in an effective manner.
  • Potential Challenges: For load balancing, auto-scaling, and actual-time resource allocation, create methods.

Energy Efficiency

  • Issue: In cloud data centers, minimizing energy usage is significant.
  • Potential Challenges: Consider the utilization of dynamic voltage and frequency scaling (DVFS), virtualization approaches, and energy-effective scheduling.
  1. Performance Optimization

Latency Reduction

  • Issue: For actual-time applications, latency has to be reduced.
  • Potential Challenges: Focus on enhancing server response times, reinforcing data storage and retrieval times, and strengthening network performance.


  • Issue: Under dynamic workloads, it is important to assure that the cloud services have the ability to adapt in an effective way.
  • Potential Challenges: Intend to create effective resource allocation techniques, distributed computing architectures, and scalable frameworks.
  1. Interoperability and Portability

Vendor Lock-In

  • Issue: Reliance on a single cloud service has to be minimized.
  • Potential Challenges: Consider the creation of multi-cloud handling tools, migration policies, and standardized APIs.

Cross-Cloud Interoperability

  • Issue: Among several cloud providers, assuring appropriate functionality and combination is crucial.
  • Potential Challenges: Correlation of services among providers must be guaranteed. For data sharing, introduce principles and protocols.
  1. Cost Management

Cost Prediction

  • Issue: The costs related to cloud resource utilization should be forecasted in a precise manner.
  • Potential Challenges: For tracking and enhancing cloud expenses, build tools. Create efficient frameworks for cost assessment.

Cost Optimization

  • Issue: Among performance and cost, identifying the balance is significant.
  • Potential Challenges: It is approachable to employ cost-effective cloud resources such as spot instances, and apply cost-aware scheduling.
  1. Data Management

Big Data Processing

  • Issue: In the cloud environments, process and examine extensive datasets in an effective way.
  • Potential Challenges: Concentrate on improving data storage and retrieval, and creating scalable data processing architectures.

Data Consistency

  • Issue: Among distributed systems, assuring data coherency is important.
  • Potential Challenges: Plan to handle data replication and integration, and apply distributed database frameworks.
  1. Network Management

Bandwidth Optimization

  • Issue: In cloud platforms, it is crucial to handle network bandwidth appropriately.
  • Potential Challenges: Focus on enhancing network traffic. For bandwidth allocation, create efficient methods.

Network Latency

  • Issue: For cloud services, minimizing network latency is examined as the major problem.
  • Potential Challenges: Improve data transfer and routing protocols, and strengthen network framework.
  1. Compliance and Legal Issues

Regulatory Compliance

  • Issue: In terms of varying regulatory needs, assuring compliance with them is significant.
  • Potential Challenges: Throughout various jurisdictions, keeping latest compliance is crucial. The compliant data processing and storage approaches have to be applied.

Data Sovereignty

  • Issue: Among various areas, handling data sovereignty needs is highly important.
  • Potential Challenges: Concentrate on adhering to regional data security rules, and assuring data localization.
  1. Emerging Technologies Integration

IoT and Edge Computing

  • Issue: Along with cloud services, combining edge computing and IoT devices is considerable.
  • Potential Challenges: Assure latency and security needs. At the edge platform, handle data processing and flow.

AI and Machine Learning

  • Issue: For machine learning and AI, utilizing cloud framework is essential.
  • Potential Challenges: Aim to combine AI services with cloud environments, enhance inference and model training, and handle extensive datasets.

Quantum Computing

  • Issue: Focus on combining the abilities of quantum computing along with cloud services.
  • Potential Challenges: Handle quantum resources, and create hybrid quantum-classical methods.
  1. User Experience and Adoption

Ease of Use

  • Issue: Specifically for users, it is important to enhance the utility of cloud environments.
  • Potential Challenges: Offer extensive assistance and documentation, and model excellent interfaces.

Training and Support

  • Issue: To utilize cloud services, assure that the users are trained in an appropriate manner.
  • Potential Challenges: It is significant to offer resources and continuous support, and create efficient training courses.
  1. Ethical Considerations

Ethical AI

  • Issue: In the cloud platforms, assure that the AI applications are created and employed in a legal manner.
  • Potential Challenges: Apply moral instructions, assure reliability, and prevent unfairness.

Surveillance Concerns

  • Issue: Through cloud service providers, plan to solve issues related to data tracking and surveillance.
  • Potential Challenges: Assure reliability in data utilization, and apply privacy-preserving approaches.

Cloud Computing Thesis Projects

Cloud Computing Thesis Ideas

Below, we present a range of rapidly evolving Cloud Computing Thesis Ideas that are currently being explored by scholars. To ensure originality and authenticity, consider obtaining your Cloud Computing Thesis Ideas from Our platform offers an extensive list of ideas, and we guarantee plagiarism-free content. If you require assistance with implementing cloud projects for your research work, we are here to support you. Kindly provide us with your details, and we will keep you updated on further developments.

  1. A digital forensic model for introspection of virtual machines in cloud computing
  2. Analysis of Security Vulnerabilities of Cloud Computing Environment Service Models and Its Main Characteristics
  3. Cost based resource allocation strategy for the cloud computing environment
  4. A Hybrid Framework to Improve Data Security in Cloud Computing
  5. Secure and Efficient Data Integrity Based on Iris Features in Cloud Computing
  6. The Impact of Vectorization on Erasure Code Computing in Cloud Storages – A Performance and Power Consumption Study
  7. A Comparative Investigation on the Use of Cloud Computing for Big Data Analytics
  8. Optimization of Cloud Computing Workload Prediction Model with Domain-based Feature Selection Method
  9. h-DDSS: Heterogeneous Dynamic Dedicated servers scheduling in cloud computing
  10. Data Security and Privacy using DNA Cryptography and AES Method in Cloud Computing
  11. Design of a MAS as Cloud Computing Service to control Smart Micro Grid
  12. Suitability of Cloud Computing for Scientific Data Analyzing Applications; An Empirical Study
  13. Optimal QoS load balancing mechanism for virtual machines scheduling in eucalyptus cloud computing platform
  14. Improving SMEs Knowledge and Performance With Cloud Computing CSF Approach : Systematic Literature Review
  15. Balancing heuristic for independent task scheduling in cloud computing
  16. A Survey and Evaluation of the Existing Tools that Support Adoption of Cloud Computing and Selection of Trustworthy and Transparent Cloud Providers
  17. An improved task scheduling and load balancing algorithm under the heterogeneous cloud computing network
  18. TRESCCA – Trustworthy Embedded Systems for Secure Cloud Computing
  19. Discussion on Big Data Processing Mode of Test Vessel Based on Cloud Computing
  20. Multiview SOA: Extending SOA using a private cloud computing as SaaS
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