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Wireless Communications and Mobile Computing
Volume 2018, Article ID 4263831, 11 pages
Research Article

Dynamic Pricing for Resource Consumption in Cloud Service

1College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, China
2NetEase, Inc., Hangzhou, China

Correspondence should be addressed to Jing Fan; nc.ude.tujz@gnijnaf

Received 24 February 2018; Accepted 19 April 2018; Published 24 May 2018

Academic Editor: Shangguang Wang

Copyright © 2018 Bin Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper studies dynamic pricing for cloud service where different resources are consumed by different users. The traditional cloud resource pricing models can be divided into two categories: on-demand service and reserved service. The former only takes the using time into account and is unfair for the users with long using time and little concurrency. The latter charges the same price to all the users and does not consider the resource consumption of users. Therefore, in this paper, we propose a flexible dynamic pricing model for cloud resources, which not only takes into account the occupying time and resource consumption of different users but also considers the maximal concurrency of resource consumption. As a result, on the one hand, this dynamic pricing model can help users save the cost of cloud resources. On the other hand, the profits of service providers are guaranteed. The key of the pricing model is how to efficiently calculate the maximal concurrency of resource consumption since the cost of providers is dynamically varied based on the maximal concurrency. To support this function in real time, we propose a data structure based on the classical B+ tree and the implementation for its corresponding basic operations like insertion, deletion, split, and query. Finally, the experiment results show that we can complete the dynamic pricing query on 10 million cloud resource usage records within 0.2 seconds on average.