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Final Year Projects in Cloud Computing

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Cloud Computing Projects for Final Year Computer Science

    Cloud computing has revolutionized how individuals and organizations access and manage computational resources, data storage, and application services. By providing scalable, on-demand access to a shared pool of configurable computing resources, cloud computing enables flexible, efficient, and cost-effective solutions for both small and large-scale applications. Cloud computing services are typically offered through three primary models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), each offering varying levels of abstraction and control.

    CloudSim is a powerful and widely used simulation tool for cloud computing environments. It allows researchers and developers to simulate and experiment with cloud infrastructure, data centers, virtual machines (VMs), applications, and resource provisioning policies. In terms of algorithms, CloudSim can be used to implement and evaluate several cloud computing algorithms related to scheduling, load balancing, resource allocation, and energy efficiency.The adoption of cloud computing has transformed traditional IT infrastructure by allowing users to rent computing power and storage on-demand, instead of investing in costly hardware and data centers. Cloud computing is particularly significant for academic and student projects, as it offers a practical, scalable solution to test, deploy, and manage applications without the need for significant upfront investment.

    Software Tools and Technologies

    • Operating System: Ubuntu 20.04 LTS 64bit / Windows 10

    • Development Tools: Apache NetBeans IDE 22 / CloudSim 4.0.0 / WorkFlowSim 1.0 / CloudAuction-2.0 / FederatedCloudSim 2.0.1

    • Language Version: JAVA SDK 21.0.2

    List Of Final Year Projects in Cloud Computing

  • • Assuring Fault-Tolerance through VM Migration-Based Load Balancing in the Cloud.
  • • Optimization of Completion Time through Efficient Resource Allocation of Task in Cloud Computing by Enhancing the Genetic Algorithm Using Differential Evolutionary Algorithm.
  • • Efficient Management and Effective Utilization of Resources through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Data Centers.
  • • Minimization of Energy Consumption in Cloud Data Centers by Applying Dynamic Programming and Analysis Tool.
  • • Game Theory Oriented Auction based Resource Allocation in Cloud Computing.
  • • Energy-Efficient Algorithms for Dynamic Virtual Machine Consolidation in Cloud Data Centers.
  • • Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment.
  • • Energy-Aware Resource Auto-Scaling Based on Allometric Scaling and Metabolic Rate Techniques for Workflow Applications In Cloud Datacenter.
  • • Dependable Scheduling with Active Replica Placement for Workflow Applications in Cloud Computing.
  • • Energy Efficient Approach to Reduce the Emission of Carbon in Data Centers.
  • • Hierarchical and Revocable Attribute-Based Encryption for Fine-Grained Access Control in Cloud Storage Services.
  • • Novel Round Robin Resource Scheduling Algorithm in Cloud Computing with Dynamic Time Quantum Allocation.
  • • Collaboration of Shortest Job First with Longest Job First Algorithms for Efficient Task Scheduling In Cloud Datacenter.
  • • Reduction of Power Consumption and Improving Resource Utilization in Cloud Infrastructure through Combination of Genetic and Meta-Heuristic Scheduling Algorithms.
  • • Clustering Algorithm based Implementation and Performance Analysis of Various VM Placement Strategies in CloudSim.
  • • Implementation of Demand Prediction by using Improved Dynamic Resource Demand Prediction and Allocation in Multi-Tenant Service Clouds.
  • • A Learning Automata-Based Algorithm for Energy and SLA Efficient Consolidation of Virtual Machines in Cloud Data Centers.
  • • Elastic and Flexible Deadline Constraint Load Balancing Algorithm for Cloud Computing.
  • • Big Media Healthcare Data Processing in Cloud: A Collaborative Resource Management Perspective.
  • • Dynamic IAAS Computing Resource Provisioning Strategy with QOS Constraint.
  • • Hybrid Task Scheduling Method for Cloud Computing by Meta Heuristic (Genetic Algorithm - Differential Evolution Method) Algorithm.
  • • Holistic Virtual Machine Scheduling in Cloud Datacenters Towards Minimizing Total Energy.
  • • Minimizing the Risk of Cloud Services Downtime using Live Migration and HEFT Upward Rank Placement.
  • • DR-Cloud for Multi-Cloud Based Disaster Recovery Service.
  • • Hybrid Green Scheduling Algorithm using Genetic Algorithm and Particle Swarm Optimization Algorithm.
  • • Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers.
  • • Energy Aware for Improving a Quality of Service parameter in Load Balancing in Cloud Computing Environment.
  • • Hybrid Heuristic Algorithm Based Energy Optimization in the Provision of On-Demand Cloud Computing Services.
  • • Task-Based System Efficient Load Balancing in Cloud Computing Using Particle Swarm Optimization.
  • • Task Based Minimizing Energy Consumption in Mobile Cloud.
  • • Towards Building Forensics Enabled Cloud through Secure Logging-as-a-Service.
  • • Energy Efficient Resource Allocation for the Effective Task Executing through VM Allocation in Cloud Computing..
  • • Genetic Algorithm-based Framework for Scheduling and Management with Adaptive Resource Tuning in Mobile Cloud.
  • • Resource Allocation on Hybrid Cloud Network using Binary Reverse Auction Algorithm in Cloud Computing.
  • • Energy-Aware Resource Auto-Scaling based on Allometric Scaling and Metabolic Rate Techniques for Workflow Applications in Cloud Datacenter.
  • • Fault Tolerant Workflow Scheduling Based on Replication and Resubmission of Tasks in Cloud Computing.
  • • Management and Monitoring System of Physical and Virtual Resources of Data Centers with Utilization Prediction Model for Energy-Aware VM Consolidation.
  • • Power Aware Resource Management of Cloud Datacenter through Multi-Objective VM Placement with Utilization Forecasting of IAAS.
  • • Optimization of Throughput using Multi objective Tasks Scheduling Algorithm for Cloud Computing.
  • • XBRLE and LZ4 Compression Algorithm for Migration of Virtual Machine in Cloud Computing.
  • • Non Clustering Algorithm Based Implementation and Performance Analysis of Various VM Placement Strategies in CloudSim.
  • • Implementation of Fault Prediction and Optimal PM Selection by using Proactive Fault-Tolerance in Cloud Computing.
  • • Resource Allocation Framework for Load Balancing using Hybrid Metaheuristic Algorithm in Cloud Computing.
  • • Memory Content Similarity for Server Consolidation by Optimizing Virtual Machine Selection and Placement.
  • • Load Balancing Algorithm based on Binary Bird Swarm Optimization for Cloud Computing.
  • • Adaptive Proactive Resource Allocation in Cloud Computing Based on Predictive Model.
  • • Efficient Adaptive Migration Algorithm in Cloud Infrastructure.
  • • Optimizing Cloud Workflow Scheduling by using Knowledge-based Adaptive Discrete Water Wave Optimization.
  • • Load Balancing with Predictive Priority-Based Dynamic Resource Provisioning Scheme in Heterogeneous Cloud Computing.
  • • Multi-Tenant Service Clouds Based Dynamic Resource Demand Prediction and Allocation.
  • • Deadline Constraint with Novel CR-PSO Approach for Multi-Objective Task Scheduling in Cloud Computing.
  • • Workflow Applications based Distributed Grey Wolf Optimizer for Scheduling in Cloud Environment.
  • • Multi-Objective Optimization for Energy and Cost-Aware Workflow Scheduling in Cloud Data Centers.
  • • Novel Round Robin Resource Scheduling Algorithm in Cloud Computing with Dynamic Time Quantum Allocation.
  • • Cloud-Based Workflow Task Scheduling: Balancing Energy Efficiency and Reliability.
  • • Geo-Distributed Data with Energy-Aware Cloud Workflow Applications Scheduling.
  • • LYRIC: Deadline and Budget-Aware Spatio-Temporal Query Optimization in Cloud Computing Environments.
  • • Enhance the Performance of Cloud Environments by using Load Balancing Strategies.
  • • Risk Management Framework for SLA-Aware Load Balancing in Cloud Computing.
  • • To Optimizing User Requirements for Cloud Data Centers by using Virtual Machine Allocation Strategy.
  • • Optimizing Resource Provisioning Based on Meta-Heuristic Population and Deterministic Algorithm by using Ant Colony Optimization and Spanning Tree.
  • • Optimizing Workflow Scheduling by using Hybrid Cost-Effective Genetic and Firefly Algorithm in Cloud Computing.
  • • Energy-Efficient VM Placement Policy in Cloud Computing Using Simulated Annealing Optimization.
  • • Discrete Water Wave Optimization Based Energy-Aware Workflow Task Scheduling in Clouds with Virtual Machine Consolidation.
  • • Optimizing Energy and Resource Efficiency by using Hybrid Heuristic Approach in Cloud Environments.
  • • Optimizing Task Scheduling and Load Balancing Techniques by using Meta Heuristic Optimization in Cloud Infrastructure Services.
  • • Energy Efficiency Based on Host Overload Management in Cloud Data Centers.
  • • Optimizing Resource Management by using Online VM Prediction based Multi-Objective Load Balancing Framework in Cloud Datacenters.
  • • QRAS: Task Scheduling based Efficient Resource Allocation in Cloud Computing.
  • • Reliability-Focused and Cost-Effective Strategy for Scientific Workflow Scheduling in Multi-Cloud Environments.

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