Ullah, Arif (2021) An improved dynamic load balancing for virtualmachines in cloud computing using hybrid bat and bee colony algorithms. Doctoral thesis, Universiti Tun Hussein Malaysia.
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Abstract
Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. In virtualization, a physical server changes into the virtual machine (VM) and acts as a physical server. Due to the large number of users sometimes the task sent by the user to cloud causes the VM to be under loaded or overloaded. This system state happens due to poor task allocation process in VM and causes the system failure or user tasks delayed. For the improvement of task allocation, several load balancing techniques are introduced in a cloud but stills the system failure occurs. Therefore, to overcome these problems, this study proposed an improved dynamic load balancing technique known as HBAC algorithm which dynamically allocates task by hybridizing Artificial Bee Colony (ABC) algorithm with Bat algorithm. The proposed HBAC algorithm was tested and compared with other stateof-the-art algorithms on 200 to 2000 even tasks by using CloudSim on standard workload format (SWF) data sets file size (200kb and 400kb). The proposed HBAC showed an improved accuracy rate in task distribution and reduced the makespan of VM in a cloud data center. Based on the ANOVA comparison test results, a 1.25 percent improvement on accuracy and 0.98 percent reduced makespan on task allocation system of VM in cloud computing is observed with the proposed HBAC algorithm.
| Item Type: | Thesis (Doctoral) |
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science and Information Technology > Department of Software Engineering |
| Depositing User: | Mrs. Sabarina Che Mat |
| Date Deposited: | 03 Feb 2022 03:08 |
| Last Modified: | 03 Feb 2022 03:08 |
| URI: | http://eprints.uthm.edu.my/id/eprint/4884 |
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