4.7 Article

Customer Perceived Value- and Risk-Aware Multiserver Configuration for Profit Maximization

Journal

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2019.2960024

Keywords

Cloud computing; customer perceived value; dynamic pricing; multiserver configuration; profit maximization; risk

Funding

  1. National Natural Science Foundation of China [61802185, 61272420]
  2. Natural Science Foundation of Jiangsu Province [BK20180470]
  3. Fundamental Research Funds for the Central Universities [30919011233]
  4. Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing [KLIGIP-2018A04]
  5. Shanghai Municipal Natural Science Foundation [16ZR1409000]

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Along with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.

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