Optimizing Resource Allocation in Cloud Computing using Machine Learning

Chittipolu Giri Pratap, Pranay Kumar (2025) Optimizing Resource Allocation in Cloud Computing using Machine Learning. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

Cloud computing enables on-demand access to shared computing resources, offering scalability, flexibility, and cost-efficiency for modern applications. However, efficient resource allocation remains a persistent challenge, as fluctuating workloads often lead to either over-provisioning, resulting in wasted resources, or under-provisioning, causing performance degradation. Traditional resource management strategies, such as rule-based and threshold-driven autoscaling, lack the predictive intelligence required to anticipate future demand accurately. This study addresses these limitations by implementing a cloud-native, predictive resource allocation framework using deep learning models integrated within Amazon Web Services (AWS). The proposed workflow involves collecting and preprocessing time-series data from over 1500 virtual machines (sourced from the GWA-T-13 Materna dataset), storing it in Amazon S3, and training forecasting models on EC2 instances using the Cloud9 IDE. Two models were implemented: a standard BiLSTM and an enhanced Attention-Centric BiLSTM Fusion model. The results demonstrate the superiority of the attention-based model, which achieved a lower Mean Squared Error (0.0043) and Root Mean Squared Error (0.0656), compared to the standard BiLSTM. The study successfully showcases how predictive modeling, when embedded in a secure and scalable cloud environment, can significantly improve resource utilization and support intelligent, cost-effective cloud infrastructure management.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jaswal, Shivani UNSPECIFIED
Uncontrolled Keywords:	Cloud Computing, Resource Allocation, Amazon Web Services (AWS), BiLSTM (Long Short-Term Memory), Attention Mechanism
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:	Ciara O'Brien
Date Deposited:	21 Nov 2025 14:39
Last Modified:	21 Nov 2025 14:39
URI:	https://norma.ncirl.ie/id/eprint/8953

Actions (login required)

View Item