Optimising makespan and resource utilisation in IaaS cloud environment

Kumar, Ankit (2023) Optimising makespan and resource utilisation in IaaS cloud environment. Masters thesis, Dublin, National College of Ireland.

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Abstract

Using Infrastructure as a Service (IaaS) and other cloud services to provide scalable resources, this research report explores the revolutionary influence of cloud computing on scientific institutions. The paper acknowledges the benefits, but it concentrates on the particular difficulties faced by scientific institutions with high computing requirements. In order to improve Quality of Service (QoS) and guarantee the timely completion of crucial activities, it highlights the necessity of intelligent resource allocation in complex scientific workflows, covering domains like biology and weather forecasting. In addition, the study discusses the notable latency in task-to-task communication and highlights the significance of managing latencyrelated issues in order to maximize overall workflow effectiveness and save costs. Robust protocols and powerful encryption are only two examples of the robust data transfer security measures that are emphasized as being crucial to protecting sensitive data while it moves to and from the cloud. The research suggests stateof-the-art task scheduling algorithms as well as sophisticated ways to help scientific firms overcome these obstacles. In the end, a new level-based allocation model for IaaS workflows called MergingWF is presented. It incorporates task merging to minimize inter-task communication overhead and lower the number of levels. Research has shown that MergingWF performs better than well-known Directed Acyclic Graph (DAG) scheduling algorithms in a variety of contexts. This confirms that MergingWF is a beneficial asset for streamlining workflow execution times in cloud environments. The study emphasizes the necessity of ongoing adaptation to changing technological environments and workflow management techniques, enabling scientific organizations to take advantage of cloud computing opportunities while skillfully handling related challenges to foster innovation and advancement across a range of scientific fields.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jaswal, Shivani 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 > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security 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:	08 Apr 2025 16:45
Last Modified:	08 Apr 2025 16:45
URI:	https://norma.ncirl.ie/id/eprint/7387

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