Indetermsoft-Set-Based D* Extra Lite Framework for Resource Provisioning in Cloud Computing
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| Pubblicato in: | Algorithms vol. 17, no. 11 (2024), p. 479 |
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| Autore principale: | |
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MDPI AG
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| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Krishnamurthy, Bhargavi |u Department of CSE, Siddaganga Institute of Technology, Tumakuru 572103, Karnataka, India | |
| 245 | 1 | |a Indetermsoft-Set-Based D* Extra Lite Framework for Resource Provisioning in Cloud Computing | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Cloud computing is an immensely complex, huge-scale, and highly diverse computing platform that allows the deployment of highly resource-constrained scientific and personal applications. Resource provisioning in cloud computing is difficult because of the uncertainty associated with it in terms of dynamic elasticity, rapid performance change, large-scale virtualization, loosely coupled applications, the elastic escalation of user demands, etc. Hence, there is a need to develop an intelligent framework that allows effective resource provisioning under uncertainties. The Indetermsoft set is a promising mathematical model that is an extension of the traditional soft set that is designed to handle uncertain forms of data. The D* extra lite algorithm is a dynamic heuristic algorithm that makes use of the history of knowledge from past search experience to arrive at decisions. In this paper, the D* extra lite algorithm is enabled with the Indetermsoft set to perform proficient resource provisioning under uncertainty. The experimental results show that the performance of the proposed algorithm is found to be promising in performance metrics such as power consumption, resource utilization, total execution time, and learning rate. The expected value analysis also validated the experimental results obtained. | |
| 653 | |a Scheduling | ||
| 653 | |a Computer centers | ||
| 653 | |a Performance measurement | ||
| 653 | |a Datasets | ||
| 653 | |a Mathematical models | ||
| 653 | |a Cloud computing | ||
| 653 | |a Decision making | ||
| 653 | |a Provisioning | ||
| 653 | |a Resource allocation | ||
| 653 | |a Algorithms | ||
| 653 | |a Quality of service | ||
| 653 | |a Resource utilization | ||
| 653 | |a Machine learning | ||
| 653 | |a Workloads | ||
| 653 | |a Uncertainty | ||
| 653 | |a Expected values | ||
| 653 | |a Energy consumption | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Resource management | ||
| 700 | 1 | |a Shiva, Sajjan G |u Department of CS, University of Memphis, Memphis, TN 38152, USA | |
| 773 | 0 | |t Algorithms |g vol. 17, no. 11 (2024), p. 479 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3132825734/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3132825734/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3132825734/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |