Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/337348
Title: | Performance Analysis of Cloud Based Application Using Neural Bio Inspired Efficient Resource Provisioning Techniques |
Researcher: | Rawat Singh Pradeep |
Guide(s): | Bhadoria Singh Robin,Saroha Pal Gyanendra |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Uttarakhand Technical University |
Completed Date: | 2021 |
Abstract: | There have been major changes in the technology and computing paradigm since last couple of decades. Now, the focus has been shifted to the service oriented based computing paradigm and Cloud Computing is one of the example based on such computational paradigm. Resource allocation and task scheduling are prime concerns in the cloud computing environment. The efficient utilization of resources includes task scheduling at the virtual machine level in the cloud computing environment. This work achieves optimal resource utilization and minimum task completion time with the resources optimal global operational cost. newlineThe thesis work presents a study on job scheduling algorithms for Virtual Machines in a cloud based environment. The presented resource provisioning or task scheduling policies were also included on BB BC Cost, power efficient Genetic Algorithm, Artificial Neural Networks GA ANN, and fault aware based neural bio inspired approaches that addresses the tasks scheduling on virtual machine in depth. newlineThe results exhibit that the proposed cost aware BB BC approach improves the average finish time by 19.18 percent. The performance metric average resource cost improves by 30.46 percent while comparing against the cost aware Genetic approach. The proposed power aware GA ANN model improves power efficiency by 13 percent, scheduling time by 77.14 percent, total execution time by 36 percent, and fault aware GA ANN model improves fault rate by 82.63 percent. Thus, the proposed optimized BB BC neural bio inspired techniques improved performance criterion significantly in comparison to the existing static, dynamic, and meta-heuristic provisioning techniques. In the future, the proposed methods could be implemented with the Kubernetes platform, which may improve the real cloud scenarios performance. The performance criterion will include reliability, cost, and time. The presented methodologies will also be tested for host level scheduling. newline |
Pagination: | 174 pages |
URI: | http://hdl.handle.net/10603/337348 |
Appears in Departments: | Department of Computer Science and Engineering |
Files in This Item:
File | Description | Size | Format | |
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01-title-page.pdf | Attached File | 17.3 kB | Adobe PDF | View/Open |
02-certificate.pdf | 386.86 kB | Adobe PDF | View/Open | |
03-abstract.pdf | 284.69 kB | Adobe PDF | View/Open | |
04-acknowledgement.pdf | 269.58 kB | Adobe PDF | View/Open | |
05-table of contents.pdf | 342.47 kB | Adobe PDF | View/Open | |
06-list of tables.pdf | 289.95 kB | Adobe PDF | View/Open | |
07-list of figures.pdf | 338.51 kB | Adobe PDF | View/Open | |
08-list of symbols.pdf | 271.81 kB | Adobe PDF | View/Open | |
09-chapter 1.pdf | 425.08 kB | Adobe PDF | View/Open | |
10-chapter 2.pdf | 622.03 kB | Adobe PDF | View/Open | |
11-chapter 3.pdf | 1.23 MB | Adobe PDF | View/Open | |
12-chapter 4.pdf | 1.65 MB | Adobe PDF | View/Open | |
13-chapter 5.pdf | 2.22 MB | Adobe PDF | View/Open | |
14-chapter 6.pdf | 548.94 kB | Adobe PDF | View/Open | |
15-chapter 7.pdf | 383.4 kB | Adobe PDF | View/Open | |
16-references.pdf | 435.85 kB | Adobe PDF | View/Open | |
17-appendix .pdf | 464.91 kB | Adobe PDF | View/Open | |
18-list of publications.pdf | 296.39 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 458.33 kB | Adobe PDF | View/Open |
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