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2017 IEEE International Conference on Web Services (ICWS) (2017)
Honolulu, Hawaii, USA
June 25, 2017 to June 30, 2017
ISBN: 978-1-5386-0752-7
pp: 130-137
Current Infrastructure-as-a-Service (IaaS) clouds offer both on-demand and reservation instance purchasing options. Users can combine these two options dynamically to serve time-varying demands while minimizing their instance acquisition costs. However, when future demands are unknown, it is far from trivial for cloud users to make optimal instance purchasing decisions. To deal with this problem, a carefully designed online algorithm can be employed to guide users in acquiring instances without any prior knowledge of future demands while guaranteeing a competitive ratio. In this paper, we propose an instance reselling model, in which a cloud user can temporarily rent out its idle reserved instances to other users through pay-as-you-go model. We also design online instance acquisition strategies which achieve a better competitive ratio than previous methods. Through extensive simulations based on both synthetic data and real-world traces, we show that our online algorithm under the proposed reselling model can outperform previous models and achieve significant cost savings.
Pricing, Algorithm design and analysis, Cloud computing, Predictive models, Prediction algorithms, Load modeling, Data models

S. Zhang, D. Yuan, L. Pan, S. Liu, L. Cui and X. Meng, "Selling Reserved Instances through Pay-as-You-Go Model in Cloud Computing," 2017 IEEE International Conference on Web Services (ICWS), Honolulu, Hawaii, USA, 2017, pp. 130-137.
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