Analysis of Time Factor with Resource Provisioning Frameworks in Cloud Environment for Improving Scheduling Performance

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.886
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Volume 13, Special Issue 3, October 2022

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pallavi wankhede(Shelke)
phD scholor student
Dr. Rekha Shahapurkar
Assistant Professor

Abstract

Time is critical in the resource provisioning process in the Cloud Computing paradigm when serving cloud resources to cloud users. It's difficult for a cloud provider to serve a large number of users while also reducing long wait times after they've submitted a request. It is possible to improve the time factor by using a systematic resource provisioning process. This paper examines several time-based resource provisioning frameworks in greater detail. Many researchers focused on various time parameters that assist cloud service providers in providing the best resource-serving services to their customers. The primary goal of this paper is to assist future researchers, as well as cloud providers in observing and selecting the best time-based resource provisioning technique also they can emphasize building a new dynamic resource provisioning paradigm in the future with this work’s observations. To validate these observations, a novel Particle Swarm Optimization (PSO) based model is designed in this text, which uses the selected time-based resource provisioning technique, and applies it to real-time cloud scenarios. It was observed that the proposed model was able to showcase better efficiency of scheduling, and optimum cloud utilization when compared with other time-based resource provisioning models for different cloud deployments

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
pallavi wankhede(Shelke), & Dr. Rekha Shahapurkar. (2022). Analysis of Time Factor with Resource Provisioning Frameworks in Cloud Environment for Improving Scheduling Performance. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.886
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