Inventory theory applied to cost optimization in cloud computing

Andrea Nodari, Jukka K. Nurminen, Christian Frühwirth

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)

Abstract

Cloud computing providers offer two different pricing schemes when renting virtual machines: reserved instances and ondemand instances. On-demand instances are paid only when utilized and they are useful to satisfy a fluctuating demand. Conversely, reserved instances are paid for a certain time period and are independent of usage. Since reserved instances require more commitment from users, they are cheaper than on-demand instances. However, in order to be cost-effective compared to on-demand instances, they have to be extensively utilized. This work focuses on finding the optimal combination of on-demand and reserved instances, such that the demand is satisfied and the costs minimized. To achieve this goal, this study introduces a stochastic model of the resources, based on Inventory Theory. The idea is to formulate the optimization problem as an inventory-keeping problem and then derive the optimal strategy. The paper evaluates the proposed model using data from an industry case, comparing the performance with a brute-force approach. The conducted experiments show that the Inventory Theory model provides accurate results and potentially allows prior research on Inventory Theory to be applied to optimal cloud provisioning.
Original languageEnglish
Title of host publicationProceedings of the 31st Annual ACM Symposium on Applied Computing - SAC '16
Place of PublicationNew York, New York, USA
PublisherAssociation for Computing Machinery ACM
Pages470-473
Number of pages4
ISBN (Print)978-1-4503-3739-7
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication

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Nodari, A., Nurminen, J. K., & Frühwirth, C. (2016). Inventory theory applied to cost optimization in cloud computing. In Proceedings of the 31st Annual ACM Symposium on Applied Computing - SAC '16 (pp. 470-473). Association for Computing Machinery ACM. https://doi.org/10.1145/2851613.2851869