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Development of a High Performance Toolkit for Modelling and Simulating Cloud Computing Environment and Application

by Mary T. Kinga, Sunday O. Adewale, Folasade M. Dahunsi
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 39
Year of Publication: 2023
Authors: Mary T. Kinga, Sunday O. Adewale, Folasade M. Dahunsi
10.5120/cae2023652900

Mary T. Kinga, Sunday O. Adewale, Folasade M. Dahunsi . Development of a High Performance Toolkit for Modelling and Simulating Cloud Computing Environment and Application. Communications on Applied Electronics. 7, 39 ( Aug 2023), 1-15. DOI=10.5120/cae2023652900

@article{ 10.5120/cae2023652900,
author = { Mary T. Kinga, Sunday O. Adewale, Folasade M. Dahunsi },
title = { Development of a High Performance Toolkit for Modelling and Simulating Cloud Computing Environment and Application },
journal = { Communications on Applied Electronics },
issue_date = { Aug 2023 },
volume = { 7 },
number = { 39 },
month = { Aug },
year = { 2023 },
issn = { 2394-4714 },
pages = { 1-15 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number39/891-2023652900/ },
doi = { 10.5120/cae2023652900 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:04:06.923261+05:30
%A Mary T. Kinga
%A Sunday O. Adewale
%A Folasade M. Dahunsi
%T Development of a High Performance Toolkit for Modelling and Simulating Cloud Computing Environment and Application
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 39
%P 1-15
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Presently, none of the current distributed (including grid and network) system simulators can offer the environment that can be directly used for modelling cloud computing environments and applications with high-performance rate, and maximum resource utilization. To overcome this challenge this research presents HiCloud: a new simulation framework that allows seamless modelling, simulation, and experimentation of emerging cloud computing infrastructures and application services. The developed system is a Cloudsim-based simulator that models cloud networks with minimum processing time and maximum resource utilization ratio. This research focuses on the resource utilization by using optimize execution time algorithm for service broker policy. It also takes into consideration the task migration approach for the load balancing algorithm that is used in the execution of tasks. The system was able to model cloud networks and application with high-performance metrics. The experimental results showed that the developed system has a better performance in terms of response time, execution time, makespan time and resource utilization ratio compared to existing systems.

References
  1. Ye Z., Liu S., Yin Y., Jin Y., (2017). User-Oriented Many- Objective Cloud Workflow Scheduling Based on an Improved Knee Point Driven Evolutionary Algorithm. Journal of Knowledge Based Systems. 12(4), 16-24.
  2. Chou F., and Chou D., (2015). Cloud Computing from the Perspective of System Analysis.International Journal of Engineering Research and Applications (IJERA), 3(5), 100-115.
  3. Mohamad R. P., Kolovos D. S., and Paige R. F., (2014). Cloud Computing Workload and Capacity Management Using Domain Specific Modelling. 14th International Conference on Modelling and Simulation. IEEE DOI 10.1109/UkSim.2014.1
  4. Kashikolaei S. M. G., HosseinabadiA. A. R., Saemi B., Shareh M. B., Sangaiah G. B. (2019). An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm. The Journal of Supercomputing. https://doi.org/10.1007/s11227-019-02816-7
  5. Arabnejad V., Bubendorfer K., and Bryan N. (2017). Scheduling deadline constrained scientific workflows on dynamically provisioned cloud resources. Future Generation Computer Systems. 14(7), 56-63.
  6. Develder G., Kumar H., and Bhoi F., (2016). Dimensioning Resilient Optical Grid / Cloud Networks. Journal of Computers. 53(4), 50-58.
  7. Owoseni M. T., (2014). Design of Government Cloud Network: Case Study Ondo State. Nigerian Journal of Technology (NIJOTECH). 35(3),608-617.
  8. Calheiros R. N., Ranjan R., De Rose C. and Buyya R., (2009). CloudSim: A Novel Framework for modelling and Simulation of Cloud Computing Infrastructures and Services. International Journal of Advanced Research in Computer Science and Software Engineering, 20(7) 67-77.
  9. Wickremasinghe B., Virogho D., and Joane F., (2010). CloudAnalyst: A CloudSim-basedVisual Modeller for Analysing Cloud. Journal of IEEE Computer Society, 16(10), 1-12.
  10. Nunez A., Vazquez-Poletti J.L., Caminero AC and Castre G.G. (2016). iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator. Journal of Grid Computing, 35(4), 30-38.
  11. Talavera O. and Santisteban R. (2015). Design of Network Infrastructure of a Cloud Data Center for Use in Health Sector. International Journal of Engineering and Technology (IJET).15(8), 141-150.
  12. Mehra T. (2012). Designing and Building a Datacenter Network: An Alternative Approach with OpenFlow. IDC, Analyze the Future. Future Generation Computer Systems, 56(10), 339-347.
  13. Taifi, G., Lu, H., and Bou J. (2013). Building a rivate HPC Cloud for Compute and Data-Intensive Applications. International Journal on Cloud Computing: Services and Architecture, 13(2), 145-154.
  14. Babaoglu O., Moreno M., and Tamburini M., (2012). Design and Implementation of Peer-to-Peer Cloud System. Università di Bologna, Dipartimento di Scienze dell’Informazione Mura A. Zamboni 7(5), 52-59.
  15. Nayak S. C., Sasmita Parida S., Tripathy C., and Pattnaik P. K., (2018). An enhanced deadline constraint based task scheduling mechanism for cloud environment. Journal of King Saud University –Computer and Information Sciences, 7(1), 33-42. https://doi.org/10.1016/j.jksuci.2018.10.009
  16. Elsherbiny S., Eldaydamony E., Alrahmawy M., and Reyad E. A., (2017). An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment. Egyptian Informatics Journal. 19(10), 21-29.
  17. Singh V., Gupta I., Prasanta K. Jana P. K., (2017). A Novel Cost-Efficient Approach for Deadline-Constrained Workflow Scheduling by Dynamic Provisioning of Resources. Future Generation Computer Systems 14 (4), 39-47.
  18. Shishido H. Y., Estrella J. C., Toledo C. F. M., Arantes, M. S., (2017). Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds. Journal of Computers and Electrical Engineering. 12(10), 15-24.
  19. Mehmi S., Harsh K., Vermab A.L., and Sangal T., (2017). Simulation modelling of cloud computing for smart grid usingCloudSim. Journal of Electrical Systems and Information Technology 4 (10), 159–172. http://dx.doi.org/10.1016/j.jesit.2016.10.004
  20. Chen W., Xie G., Li R., Bai Y., Fan C. and Li K., (2017), Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Future Generation Computer Systems 14(1), 1-8.
  21. Adhikari M., Amgoth T., and Srirama S. N., (2019). A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging Trends. Future Generation Computer Systems 68(4), 1-36. https://doi.org/10.1145/3325097
  22. Choudhary A., Gupta I., Singh V., and Jana P. K., (2018). A GSA based Hybrid Algorithm for Bi-objective Workflow Scheduling in Cloud Computing. Future Generation Computer Systems 13 (17), 29-39.
  23. Igor L. S., Luci P., Flavia C., Delicato H., Gabriel M., O., Claudio M. F., Samee U. K., Albert Y. Z., (2019). Zeus: A resource allocation algorithm for the cloud of sensors. Future Generation Computer Systems. 92 (20), 564-581.
  24. Wang W., Zeng G., Tang D., and Yao J., (2017). Cloud – DLS: Dynamics Trusted Scheduling For Cloud Computing.Journal of Expert System with Applications. 40(16), 2310-2317.
  25. Seenuvasan P., Kannan A., and Varalakshmi P. (2017). Agent-Based Resource Management in a Cloud Environment. Journal of Applied Mathematics & Information Sciences, 11(3), 777-788
  26. Er-Raji N., Benabbou F. and Eddaoui A., (2016). Task Scheduling Algorithms in the Cloud Computing environment: Survey and Solutions. International Journal of Advanced Research in Computer Scienceand Software Engineering, 6(1), 604-608.
Index Terms

Computer Science
Information Sciences

Keywords

Simulation Cloud Computing Modeling High Performance Toolkit.