Cluster Computing

, Volume 21, Issue 1, pp 1–13 | Cite as

Support for spot virtual machine purchasing simulation

  • Ao ZhouEmail author
  • Shangguang Wang
  • Qibo Sun
  • Jinglin Li
  • Qinglin Zhao
  • Fangchun Yang


With the rapid progress of cloud computing technology, a growing number of big data application providers begin to deploy applications on virtual machines rented from infrastructure as a service providers. Current infrastructure as a service provider offers diverse purchasing options for the application providers. There are mainly three types of purchasing options: reserved virtual machine, on-demand virtual machine and spot virtual machine. The spot virtual machine is a specific type of virtual machine that employs a dynamic pricing model. Because can be stopped by the infrastructure as a service providers without notice, the spot virtual machine is suitable for large-scale divisible applications, such as big data analysis. Therefore, spot virtual machine is chosen by many big data application providers for its low rental cost per hour. When spot virtual machine is chosen, a major issue faced by the big data application providers is how to minimize the virtual machine rental cost while meet service requirements. Many optimal spot virtual machine purchasing approaches have been presented by the researchers. However, there is a shortage of simulators that enable researchers to evaluate their newly proposed spot virtual machine purchasing approach. To fill this gap, in this paper, we propose SpotCloudSim to support for dynamic virtual machine pricing model simulation. SpotCloudSim provides an extensible interface to help researchers implement new spot virtual machine purchasing approach. In addition, SpotCloudSim can also study the behavior of the newly proposed spot virtual machine purchasing approaches. We demonstrate the capabilities of SpotCloudSim by using three spot virtual machine purchasing approaches. The results indicate the benefits of our proposed simulation system.


Cloud computing Virtual machine Big data analysis Simulator Dynamic pricing model 



This work is supported by NSFC (61602054), NSFC (61472047), and Beijing Natural Science Foundation (4174100). This work is supported by Macao FDCT-MOST Grant 001/2015/AMJ and Macao FDCT Grant 104/2014/A3.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Research involving animal and human rights

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Ao Zhou
    • 1
    Email author
  • Shangguang Wang
    • 1
  • Qibo Sun
    • 1
  • Jinglin Li
    • 1
  • Qinglin Zhao
    • 2
  • Fangchun Yang
    • 1
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Faculty of Information TechnologyMacau University of Science and TechnologyTaipaChina

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