|S.No.||Name of the Tools||Area of Research||Solutions Topics|
|1||CloudSim 4.0||Cloud Computing||1. Modeling and simulation of Cloud Datacenter.2. Inter-Network of Cloud datacenter.
3. Optimization of resource utilization.
4. Dynamic resource provisioning and Scheduling.
5. Dynamic migration of task.
6. Load balancing.
7. Monitoring application execution.
8. Resource Management.
9. Resource Auto scaling.
10. Power management of data center.
11. Dynamic creation of new entities.
12. Revenue maximization of data center.
13. Avoidance of service level agreements.
14. VM Placement and Consolidation.
15. Deadline constrained dynamic scheduling.
16. Auction based resource scaling and provisioning in the datacenter.
17. Game-Based Price Bidding for Cloud Resource Provisioning.
|2||WorkflowSim 1.0||Scientific workflow tasks execution in cloud computing||1. Scientific workflow tasks scheduled in hybrid data centers.2. Workflow scheduling with security and deadline constraints.
3. Fault-tolerance for reliable workflows on heterogeneous resource Clouds.
4. Horizontal/Vertical clustering of workflows.
5. Dynamic reclustering
6. Overhead robustness of DAG scheduling.
7. Prioritization of job scheduling.
8. Scientific workflow scheduling policies.
9. Scientific workflow planning policies.
|3||EdgeCloudSim||Mobile Edge Computing||1. Modelling and implementation of mobile cloud computing.2. Multi-resource allocation for mobile cloud computing.
3. Resource management.
4. Task Offloading.
5. Resource Allocation for Mobile-Edge Computing Networks.
6. Fault-Tolerance Approach to Enhance Cloud Service Reliability.
7. Resource Provisioning in the Edge for IoT Applications.
8. Workload aware Vm Consolidation.
9. Machine learning model for energy-efficient Edge scheduling.
10. Multi-user Multi-task Computation Offloading in Edge Cloud Computing.
|4||iFogSim||IOT/Fog Computing||1. Modeling of an IOT environment.2. Comparison of resource management policies.
3. To meet application level QoS.
4. Minimizing resource and energy wastage.
5. Power saving models.
6. Reduction of execution cost.
7. Application scheduling policies across edge and cloud environment.
8. Measuring performance metrics at edge devices, cloud datacenter and n/w links.
9. Infrastructure Monitoring.
10. Co-execution of multiple applications.
11. Migration of application modules.
12. Pluggable resource management policies.
|5||FederatedCloudSim||Evaluation of SLA, scheduling and brokering strategies on Cloud Federation level.||1. Simulation of different federation scenarios having each one to many data centers and vCSP(Virtual Cloud Service Providers) which act as service brokers without resources (DCs) of their own.2. Fully automized SLA management.
3. Three tier VM scheduling.
4. Resource over provisioning (RAM and CPU).
5. Processing of real world cloud workload traces.
6. Cloud auctioning platform.
7. Finance model (CloudAccount) for calculating revenues and fines for SLA-breaches.
8. Comprehensive monitoring and logging (including graphical analysis of VM migrations using Gephi2).
9. SLA, scheduling and brokering strategies.
10. Conduct auctions to select an appropriate federated partner for running outsourced virtual machines.
|6||CloudSimSDN||Software-Defined Networking enabled Cloud Computing.||1. Elasticity and Scalability of cloud resources.2. GUI Simulation configuration.
3. VM Management.
4. Network Management.
5. Virtual topology(Vm,virtual Channel)
6. Dynamic bandwidth allocation.
7. Channel allocation.
8. Fast network recovery.
9. Traffic Management.
10. Monitoring data transmission time between hosts.
|7||CloudAuction 2.0||Implementing auction-based mechanism in Cloud Computing.||1. To handle auction based services.2. The auction is held based on cost for CPU, MIPS, bandwidth, Ram, size, and etc. are regarded on Vm side.
3. The benefits and satisfaction of both users and providers by applying relevant attributes in cloud environments.