Task scheduling in cloud computing using hybrid genetic algorithm and bald eagle search (GA-BES)
Cloud computing is on-demand resources available for the computer system, especially in the data storage without management from the human. Cloud computing one of the fastest growing technologies and now managing file and services on the local storage devices is no longer use because of the cloud co...
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| Format: | Academic Exercise |
| Language: | English English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/33195/1/Task%20scheduling%20in%20cloud%20computing%20using%20hybrid%20genetic%20algorithm%20and%20bald%20eagle%20search%20%28ga-bes%29.24pages.pdf https://eprints.ums.edu.my/id/eprint/33195/2/Task%20scheduling%20in%20cloud%20computing%20using%20hybrid%20genetic%20algorithm%20and%20bald%20eagle%20search%20%28ga-bes%29.fulltext.pdf https://eprints.ums.edu.my/id/eprint/33195/ |
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| Summary: | Cloud computing is on-demand resources available for the computer system, especially in the data storage without management from the human. Cloud computing one of the fastest growing technologies and now managing file and services on the local storage devices is no longer use because of the cloud computing that can do the same things over the internet where anyone and everyone can access to it. Task scheduling and resource allocation are essential aspects of cloud computing. This Study proposes task scheduling in cloud computing using a hybrid genetic algorithm, and bald eagle search proposed to solve the task scheduling problem. The genetic algorithm was widely used because of its accuracy and simplicity. However, it will become slower in some instances that include a more significant problem size. Hence, Bald Eagle Search (BES) can increase efficiency and performance because it provides an efficient scheduling mechanism. The natural evolution optimization algorithm which is genetic algorithm can be improve by combining the nature meta-heuristic algorithms which is bald eagle search to improve the makespan of genetic algorithm using cloudsim that need to be implement on the eclipse platform. As for the running method the compilation of the code will be run by using cmd and Ant Apache and the total average result of 30 simulation will be view on the web base application will be run using xampp. |
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