Hedonic pricing of Cloud Computing Services

Caesar Wu, Adel Nadjaran Toosi, Rajkumar Buyya, Kotagiri Ramamohanarao

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)


Cloud service providers (CSP) and consumers demand to forecast the cloud price to optimize their business strategy. However, the cloud pricing based on consumers' willingness to pay (W2P) becomes challenging due to the subjectiveness of consumers' experiences and implicit values of some non-marketable prices, such as burstable CPU, dedicated server, and cloud data center global footprints. Many existing pricing models often cannot support value-based pricing. We propose a novel solution based on value-based pricing that not only considers how much does the service cost (or intrinsic values) to a CSP but also how much customer is willing to pay (or extrinsic values). We demonstrate that the cloud extrinsic values become one of the competitive advantages for CSPs to lead the cloud market and increase the profit margin. We show that our model can capture the value of non-marketable price. This value is about 43.4% on average above the baseline. We also show that Average Annual Growth Rate (AAGR) of Amazon Web Services' (AWS) is about -20.0% per annum between 2008 and 2017, ceteris paribus. In comparison with Moore's law (-50% per annum), it is at a far slower pace. We argue this value is Moore's law equivalent in the cloud.

Original languageEnglish
Pages (from-to)182-196
Number of pages15
JournalIEEE Transactions on Cloud Computing
Issue number1
Publication statusPublished - Jan 2021


  • Cloud Characteristics
  • Extrinsic
  • Hedonic Pricing
  • Intrinsic Variables
  • Time Dummy

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