Private cloud deployment model selection for cost efficiency: a business perspective
Abstract
Purpose
This study aims to enhance an enterprise’s private cloud services by optimally determining the ownership of cloud computing resources and responsibility for maintenance and operations. The core objective is to identify the most cost-effective private cloud deployment model at the intersection of technology and business considerations.
Design/methodology/approach
This study evaluates three ownership and responsibility models, each encompassing decisions related to candidate data center locations, resource provisioning, and demand placements. Drawing from the cloud computing literature, these models are referred to as deployment models. The research formulates a private cloud deployment model selection problem and introduces an established Lagrangian-relaxation-based optimization approach, combined with a novel greedy relieving-pooling heuristic, to facilitate model selection.
Findings
This study identifies the optimal deployment model for a representative instance using real test-bed data from the US, demonstrating the private cloud deployment model selection problem. Various numerical examples are analyzed to explore the influence of environmental parameters. Generally, the virtual PC model is optimal for low demand arrival rates and resource requirements, while the on-premises PC model is preferable for higher values of these parameters. Additionally, the virtual PC model is found to be optimal when enroute latency coefficients are large.
Originality/value
This study contributes to the literature by formulating an optimization problem that integrates performance, financial, and assurance metrics for enterprises. The introduction of a solution approach enables enterprises to make informed decisions regarding ownership and responsibility design. The study effectively bridges the gap between academic research and industry demands from a business perspective.
Keywords
Acknowledgements
This work was supported by Natural Science Foundation of Sichuan Province [grant number 2022NSFSC0540].
Citation
Liu, B., Song, J. and Geng, W. (2024), "Private cloud deployment model selection for cost efficiency: a business perspective", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-02-2024-0430
Publisher
:Emerald Publishing Limited
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