Optimizing Workload Scheduling And Cost Efficiency Using Cloud Orchestration Frameworks in Multi-Cloud Environments

Sawant, Rushikesh Lahanu (2025) Optimizing Workload Scheduling And Cost Efficiency Using Cloud Orchestration Frameworks in Multi-Cloud Environments. Masters thesis, Dublin, National College of Ireland.

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Abstract

The investigation involves an empirical analysis of workload schedules and the topics of cost optimisation and cost comparison in Kubernetes-based multi-cloud environments that utilise Amazon Web Services (AWS), Microsoft Azure, IBM Cloud, and Google Cloud Platform (GCP). The paper examines this by looking at the benefits of using real-time metrics (using Datadog to monitor performance, Big Query to analyse costs, and Google Sheets to display the cost metrics in real time visually) to make decisions regarding where to place workloads across providers. The same workloads were run in a multi-cloud scale setting, and the results measured latency, availability, throughput, cost and the complexity of operations. The findings show that comparison between multiple cloud providers shows large savings in costs and resiliency through dynamic elastic exploitation of provider-specific cost advantages and performance differences through workloads, and single-cloud deployments provide simplified operational management and predictable behaviour on stable workloads. Nonetheless, the multi-cloud orchestration complexity is key to the need to have powerful data integration and observability frameworks. The paper has delivered benchmarks, cost-performance trade-off analysis, and useful recommendations to schedule workloads in Kubernetes, making it operational and of use to the cloud practitioners in having actionable advice to inform data-driven deployments.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Heeney, Sean 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
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	30 Mar 2026 14:58
Last Modified:	30 Mar 2026 14:58
URI:	https://norma.ncirl.ie/id/eprint/9259

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