412
Views
50
CrossRef citations to date
0
Altmetric
Original Articles

An efficient approach for improving virtual machine placement in cloud computing environment

ORCID Icon, &
Pages 1149-1171 | Received 24 May 2016, Accepted 19 Mar 2017, Published online: 05 Apr 2017
 

Abstract

The ever increasing demand for the cloud services requires more data centres. The power consumption in the data centres is a challenging problem for cloud computing, which has not been considered properly by the data centre developer companies. Especially, large data centres struggle with the power cost and the Greenhouse gases production. Hence, employing the power efficient mechanisms are necessary to optimise the mentioned effects. Moreover, virtual machine (VM) placement can be used as an effective method to reduce the power consumption in data centres. In this paper by grouping both virtual and physical machines, and taking into account the maximum absolute deviation during the VM placement, the power consumption as well as the service level agreement (SLA) deviation in data centres are reduced. To this end, the best-fit decreasing algorithm is utilised in the simulation to reduce the power consumption by about 5% compared to the modified best-fit decreasing algorithm, and at the same time, the SLA violation is improved by 6%. Finally, the learning automata are used to a trade-off between power consumption reduction from one side, and SLA violation percentage from the other side.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 373.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.