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Peter, Jackson (2022) Improving the Auto scaling mechanism in Cloud computing environment using Support Vector regression and Bi-LSTM. Masters thesis, Dublin, National College of Ireland.
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Abstract
The availability, reliability and on-demand feature of the cloud computing system has attracted many users to the cloud computing platforms, where the resources can be dynamically allocated to the instance as per the workload demand. An efficient auto-scaling mechanism allocates and de-allocates the resources to meet the performance targets in changing workload conditions. Also, it helps to minimize the resources cost as well as making the resource availability on time in order to maintain the quality of service. In this work, I have developed a cloud-based framework using Python language and experimented with 3 different machine learning and deep learning algorithms (Linear regression, Support Vector regression and Bi-directional LSTM) for implementing the auto-scaling mechanism. After the comparative analysis, I have obtained better results using support vector regression and Bi-LSTM algorithms for dynamic and non-linear behaviour in cloud computing environment.
| Item Type: | Thesis (Masters) |
|---|---|
| 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 > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Tamara Malone |
| Date Deposited: | 30 Nov 2022 15:31 |
| Last Modified: | 08 Mar 2023 14:46 |
| URI: | https://norma.ncirl.ie/id/eprint/5947 |
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