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Salvi, Rohit Rajesh (2022) Optimizing the load balancing efficiency using enhanced genetic algorithm in cloud computing. Masters thesis, Dublin, National College of Ireland.
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Abstract
Cloud computing (CC) technology has received a great deal of interest in recent years from both academics and businesses. To boost the scalability and flexibility of cloud Data Centers (DC), load-balancing solutions are essential. One of the most important concerns in a distributed computing system is the Load Balancing (LB) technique. Because a cloud provider must service several customers in a cloud setting, task scheduling with efficient LB is a key issue in CC. Many strategies, algorithms, and methodologies have been developed over the years to enhance the LB approach in CC. These strategies are primarily concerned with minimizing execution time, cutting energy consumption and overall resource utilization, and rapid task scheduling by swiftly distributing the tasks in a cluster of Virtual Machines (VM). Because no consideration is given to the present load of the VM, the VM in the cluster may begin to experience overloading concerns. As a result, there is a need for a technique that considers not only the load of the VM but also resource, time, and energy metrics. To apply flexible and effective LB in a cloud system, this research suggests an Enhanced Genetic Algorithm (EGA) based on the Genetic Algorithm (GA). The goal of this method is to analyze the load of the VMs and allocate jobs to VMs that will not get overloaded. Based on performance parameters such as resource usage, energy, and time consumption, the algorithm's results will be compared to Particle Swarm Optimization (PSO), a popular LB technique. The results demonstrate that, as the number of cloudlets increases, EGA's execution time, resource utilization, and energy consumption are much lower than those of PSO.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mijumbi, Rashid UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Tamara Malone |
| Date Deposited: | 19 Apr 2023 13:58 |
| Last Modified: | 19 Apr 2023 13:58 |
| URI: | https://norma.ncirl.ie/id/eprint/6488 |
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