An efficient and energy-aware cloud consolidation algorithm for multimedia big data applications

Jong Beom Lim, Heon Chang Yu, Joon Min Gil

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


It is well known that cloud computing has many potential advantages over traditional distributed systems. Many enterprises can build their own private cloud with open source infrastructure as a service (IaaS) frameworks. Since enterprise applications and data are migrating to private cloud, the performance of cloud computing environments is of utmost importance for both cloud providers and users. To improve the performance, previous studies on cloud consolidation have been focused on live migration of virtual machines based on resource utilization. However, the approaches are not suitable for multimedia big data applications. In this paper, we reveal the performance bottleneck of multimedia big data applications in cloud computing environments and propose a cloud consolidation algorithm that considers application types. We show that our consolidation algorithm outperforms previous approaches.

Original languageEnglish
Article number184
Issue number9
Publication statusPublished - 2017 Sept 1

Bibliographical note

Funding Information:
Acknowledgments: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03933370 and NRF-2015R1D1A1A01061373).

Publisher Copyright:
© 2017 by the authors.


  • Big data
  • Cloud computing
  • Cloud consolidation
  • Multimedia application
  • Virtual machine

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Chemistry (miscellaneous)
  • General Mathematics
  • Physics and Astronomy (miscellaneous)


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