Cloud Resource Management using SLA parameters with RFD Algorithm

Arulappan, John Kennady (2024) Cloud Resource Management using SLA parameters with RFD Algorithm. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the fast-evolving area of cloud computing, cloud resource management is a significant challenge faced during resource allocation and meeting of Service Level Agreements (SLAs) is crucial to maintaining users’ trust and delivering high-quality services. Most of the existing methodologies focus on execution time and cost optimization to the neglect of SLA compliance. In this work, a novel multi objective optimization framework is proposed with an improved River Formation Dynamics (RFD) algorithm with dynamic SLA parameters, machine learning (ML) based workload classification and energy-aware allocation for cloud resource management. The classified system integrates ML based workload classification to achieve 92-100% classification accuracy with respect to the different types of workloads tested. An optimal balance of SLA compliance, cost effectiveness, and energy efficiency is achieved through a weighted objective function. The simulation performance is then evaluated through experimental analysis in CloudSim which simulates 3 heterogeneous hosts and 5 VMs processing 100 cloudlets achieving 70% SLA compliance over all workload types. With the power consumption ranging between 120W–12.990kW, the system achieves optimal resource utilization of 85.2% CPU utilization for compute intensive tasks. The workloads too were distributed balanced among CPU-intensive (34%), memory-intensive (33%) and I/O intensive (33%) tasks and the execution pattern of the jobs is predictable with I/O intensive jobs completing first (1165.06s), followed by memory and CPU intensive jobs (2131.36s and 2793.87s, respectively).

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
Uncontrolled Keywords:	Resource management; river formation dynamics; service level agreement; machine learning; multi-objective Optimization
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:	14 Jul 2025 14:23
Last Modified:	14 Jul 2025 14:23
URI:	https://norma.ncirl.ie/id/eprint/8082

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