An economic agent maximizing cloud provider revenues under a pay-as-you-book pricing model

  • Authors:

  • Felipe Díaz Sánchez;Elias A. Doumith;Sawsan Al Zahr;Maurice Gagnaire

  • Affiliations:

  • Networks and Computer Sciences Department, Institut Mines-Telecom - Telecom ParisTech - LTCI CNRS, Paris Cedex 13, France;Networks and Computer Sciences Department, Institut Mines-Telecom - Telecom ParisTech - LTCI CNRS, Paris Cedex 13, France;Networks and Computer Sciences Department, Institut Mines-Telecom - Telecom ParisTech - LTCI CNRS, Paris Cedex 13, France;Networks and Computer Sciences Department, Institut Mines-Telecom - Telecom ParisTech - LTCI CNRS, Paris Cedex 13, France

  • Venue:
  • GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
  • Year:
  • 2012

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Abstract

The Cloud computing paradigm offers the illusion of infinite resources accessible to end-users anywhere at anytime. In such dynamic environment, managing distributed heterogeneous resources is challenging. A Cloud workload is typically decomposed into advance reservation and on-demand requests. Under advance reservation, end-users have the opportunity to reserve in advance the estimated required resources for the completion of their jobs without any further commitment. Thus, Cloud service providers can make a better use of their infrastructure while provisioning the proposed services under determined policies and/or time constraints. However, estimating end-users resource requirements is often error prone. Such uncertainties associated with job execution time and/or SLA satisfaction significantly increase the complexity of the resource management. Therefore, an appropriate resource management by Cloud service providers is crucial for harnessing the power of the underlying distributed infrastructure and achieving high system performance. In this paper, we investigate the resource provisioning problem for advance reservation under a Pay-as-you-Book pricing model. Our model offers to handle the extra-time required by some jobs at a higher price on a best-effort basis. However, satisfying these extra-times may lead to several advance reservations competing for the same resources. We propose a novel economic agent responsible for managing such conflicts. This agent aims at maximizing Cloud service provider revenues while complying with SLA terms. We show that our agent achieves higher return on investment compared to intuitive approaches that systematically prioritize reserved jobs or currently running jobs.