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Malviya, Shantanu (2024) Enhancing Load Balancing Efficiency In Dynamic Workload Environments Using Enhanced Genetic Algorithm and Machine Learning. Masters thesis, Dublin, National College of Ireland.
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Abstract
Cloud computing has gained significant popularity in recent times, providing scalable and elastic IT-enabled resources ’as a service’ to customers. There still exists issues with load balancing in cloud computing while handling dynamically dependent workloads. The improper distribution of loads in the cloud environment ultimately results in depreciation of overall system performance. To achieve this, multiple studies utilising various methods of both Enhanced Genetic Algorithm (EGA) and Machine Learning (ML) have been carried out. These methods often lack the vision and execution of integrating both technologies and consider the outcomes of this integration. This study aims to achieve more effective load balancing in cloud computing networks using an Enhanced Genetic Algorithm (EGA) combined with multiple Machine Learning (ML) targeted for the real-time load balancing problems.
An algorithm that has the capabilities to handle dynamic workloads and is equipped with advanced technologies that make use of predictive analysis can act as research area and a concept which can potentially resolve the issues with current present algorithms. This research proposes an EGA with Selection, Crossover and Mutation techniques in a virtual simulation of a Datacenter with Virtual Machines (VMs) that is capable of providing outputs as Resource Utilisation and Execution Time. The algorithm was executed for ‘n’ iterations to gather dataset for ML model training. The various Machine Learning models results showcased Linear Regression outperforming other techniques to achieve the objective.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Arun, Shreyas Setlur 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 Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 03 Jul 2025 13:40 |
| Last Modified: | 03 Jul 2025 13:40 |
| URI: | https://norma.ncirl.ie/id/eprint/8034 |
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