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Enhancing Cloud Efficiency using Intelligent Autoscaling Algorithms

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Rahane, Pushkar Deodatta (2023) Enhancing Cloud Efficiency using Intelligent Autoscaling Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cloud computing has revolutionized the way data and applications are managed, offering scalable, flexible, and cost-effective solutions. A critical aspect of cloud computing is autoscaling, the ability to dynamically adjust computing resources to match demand. In this research, we explored the challenges of autoscaling by implementing and evaluating three distinct algorithms: Decision Tree, Random Forest, and LSTM, within a simulated cloud environment. The study utilized metrics such as Total Response Time, Total Delayed Load, Prediction Error of Models, and Total Wastage of Resources to evaluate the algorithms’ effectiveness. The results revealed that LSTM achieved the best performance in response time and prediction accuracy, while Random Forest excelled in resource utilization. Our research contributes valuable insights into the dynamics of autoscaling, paving the way for more efficient and responsive cloud systems. The findings also highlight potential avenues for future research and optimization in autoscaling techniques.

Item Type: Thesis (Masters)
Supervisors:
Name
Email
Kazmi, Aqeel
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: 18 Oct 2024 15:59
Last Modified: 18 Oct 2024 15:59
URI: https://norma.ncirl.ie/id/eprint/7102

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