Maximizing Cloud Revenue using Dynamic Pricing of Multiple Class Virtual Machines

The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has significant portion of business values of finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been proposed as an elegant...

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Bibliographic Details
Published in:IEEE transactions on cloud computing Vol. 9; no. 2; pp. 682 - 695
Main Authors: Alzhouri, Fadi, Agarwal, Anjali, Liu, Yan
Format: Journal Article
Language:English
Published: Piscataway IEEE Computer Society 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cloud computing
Computational modeling
Data centers
dynamic price
Dynamic programming
Leasing
Linear programming
Markov processes
Maximization
maximizing revenue
multiple classes
Optimization
Pricing
Revenue
spot instances
Virtual environments
virtual machines
Virtual machining
ISSN:2168-7161, 2372-0018
Online Access:Get full text
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Summary:The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has significant portion of business values of finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been proposed as an elegant solution to overcome these challenges, with the ultimate goal to achieve greater profits. However, previous studies on recent spot pricing schemes reveal artificial pricing policies that do not comply with the dynamic nature of these phenomena. Motivated by these facts, this paper investigates dynamic pricing of stagnant resources in order to maximize cloud revenue. Specifically, our proposed approach manages multiple classes of virtual machines in order to achieve the maximum expected revenue within a finite discrete time horizon. For this sake, the proposed approach leverages the Markov decision processes with a number of properties under optimum controlling conditions that characterize a model's behaviour. Further, this approach applies approximate stochastic dynamic programming using linear programming to create a practical model. Experimental results confirm that this approach of dynamic pricing can scale up or down the price efficiently and effectively, according to the stagnant resources and the load thresholds. These results provide significant insights to maximizing the IaaS cloud revenue.
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ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2018.2878023