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Simple Pricing Schemes for the Cloud

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10660)


The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per time step for jobs of any length achieves at least \(50\%\) of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.


  • Simple Pricing Scheme
  • Revenue Guarantees
  • Truthful Mechanism
  • Approximate Welfare
  • Individual Price

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  1. 1.

    Amazon recently started offering a product called “defined duration spot instances” where users can specify a duration in hourly increments up to six hours [2]. Indeed, the price per hour of this product increases as the number of hours increases.

  2. 2.

    For technical reasons, we will deviate slightly from the usual notion of cumulative distribution function. In particular, if y is a random variable drawn from a distribution, then we define its cumulative distribution function F(x) as \(\text {Pr}[y<x]\) instead of the usual \(\text {Pr}[y\le x]\). This will only be important when we deal with discrete distributions.

  3. 3.

    For the offline optimal welfare, we compute the limit of the expected average offline optimal welfare per time step as the time horizon grows.


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Correspondence to Warut Suksompong .

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Kash, I.A., Key, P., Suksompong, W. (2017). Simple Pricing Schemes for the Cloud. In: R. Devanur, N., Lu, P. (eds) Web and Internet Economics. WINE 2017. Lecture Notes in Computer Science(), vol 10660. Springer, Cham.

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