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Towards Sustainable IaaS Pricing

  • Philipp Berndt
  • Andreas Maier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8193)

Abstract

Cloud computing has the potential to improve resource efficiency by consolidating many virtual computers onto each physical host. This economization is based on the assumption that a significant percentage of virtual machines are indeed not fully utilized. Yet, despite the much acclaimed pay-only-for-what-you-use paradigm, public IaaS cloud customers are usually still billed by the hour for virtual systems of uncertain performance rather than on the basis of actual resource usage. Because ensuring and proving availability of defined performance for collocated multi-tenant VMs poses a complex technical problem, providers are still reluctant to provide performance guarantees. In lack thereof, prevailing cloud products range in the low price segment, where providers resort to overbooking and double selling capacity in order to maintain profitability, thereby further harming trust and cloud adoption. In this paper we argue that the predominant flat rate billing in conjunction with the practice of overbooking and its associated mismatch between actual costs and billed posts results in a substantial misalignment between the interests of providers and customers that stands in the way of trustworthy and sustainable cloud computing. On these grounds, we propose a hybrid IaaS pricing model that aims to avoid these problems in a non-technical fashion by shifting to consumption based billing on top of credible minimum performance. Requiring only measures that can be obtained with a low degree of technical complexity as well as a moderate amount of trust, the approach aspires to be more sustainable, practicable and billable than common practice even without the use of complex should-I verifiability.

Keywords

Cloud Computing Virtual Machine Cloud Provider Rate Billing Resource Accounting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Philipp Berndt
    • 1
  • Andreas Maier
    • 1
  1. 1.Zimory GmbHBerlinGermany

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