Prediction of Resource Utilization in Cloud Computing using Machine Learning

Shaikh, Ruksar (2024) Prediction of Resource Utilization in Cloud Computing using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

In today’s modern computing infrastructure, cloud computing has emerged as a pivotal paradigm, offering scalability and flexibility to satisfy the demands of a wide variety of specific applications. Maintaining optimal performance and cost-effectiveness inside cloud settings continues to be a significant problem, and one of the most important challenges is efficient resource utilisation. A resource utilisation prediction system is required to aid the resource allocator in providing optimal resource allocation. Accurate prediction is difficult in such a dynamic resource utilisation. The applications of machine learning techniques are the primary emphasis of this research project, which aims to predict resource utilisation in cloud computing systems. The dataset GWA-T-12 bitbrains (from distributed datacenter) have provided the data of timestamp, cpu usage, network transmitted throughput and Microsoft Azure traces has provided the data of cpu usage of cloud server. To predict VM workloads based on CPU utilisation, we use machine learning models such as Linear Regression, Decision Tree Regression, Gradient Boosting Regression, and Support Vector Regression, as well as deep learning architectures such as Long Short-Term Memory (LSTM) and Bi-directional Long Short-Term Memory (BiLSTM). The Python programming language is used to carry out the implementation within the Google Colab environment. Bi-directional Long Short Term Memory approach is considered more effective as compared to other models in terms of CPU Utilisation and Network Transmitted Throughput as it R2 score is close to 1 hence can produce more accurate results.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Shaguna 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 > 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:	10 Apr 2025 15:11
Last Modified:	10 Apr 2025 15:11
URI:	https://norma.ncirl.ie/id/eprint/7412

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