System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO)
Cloud is a collection of interconnected computers which varies from personal computer to server. In cloud computing, program system is an important issue which needs to be managed in a better way. Program system assigns user tasks to the suitable Virtual Machines in order to attain Quality of Servic...
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| Format: | Academic Exercise |
| Language: | en en |
| Published: |
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/33260/1/SYSTEM%20PROGRAM%20MANAGEMENT%20ENVIRONMENT%20IN%20CLOUD%20COMPUTING%20USING%20HYBRID%20GENETIC%20ALGORITHM%20AND%20MOTH%20FLAME%20OPTIMIZATION%20%28GA-MFO%29.24pages.pdf https://eprints.ums.edu.my/id/eprint/33260/2/SYSTEM%20PROGRAM%20MANAGEMENT%20ENVIRONMENT%20IN%20CLOUD%20COMPUTING%20USING%20HYBRID%20GENETIC%20ALGORITHM%20AND%20MOTH%20FLAME%20OPTIMIZATION%20%28GA-MFO%29.pdf https://eprints.ums.edu.my/id/eprint/33260/ |
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| Summary: | Cloud is a collection of interconnected computers which varies from personal computer to server. In cloud computing, program system is an important issue which needs to be managed in a better way. Program system assigns user tasks to the suitable Virtual Machines in order to attain Quality of Service (QoS) parameters. Optimization algorithms can be used to solve Non-deterministic Polynomial (NP) hard problem like system management. In this project, Genetic Algorithms (GA) is combine Moth Flame Optimization (MFO) to improve the cloud computing environment. The optimization old system program for cloud computing as a challenging issue has been considered as the NP-hard problem in cloud computing environment. This project present a system program algorithm based on Moth Flame Optimization (MFO) algorithm to assign an optimal set of system program to meet the satisfaction of quality of service requirements of cloud computing in such a way that the total execution time of tasks is minimized. The minimization of system execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed algorithm are considered as objective functions. The result of the proposed algorithm found, the optimal solution for the system program of management and equal distribution of tasks to cloud has been provided, and less total execution time consumption has been achieved compared with other algorithm. |
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