Publication Type:

Journal Article

Source:

Indian Journal of Science and Technology, Indian Society for Education and Environment, Volume 9, Issue 29, Number 79768 (2016)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983546553&partnerID=40&md5=423c9f3084cebad7514e6a4032e29b13

Abstract:

<p>Objectives: Hardware virtualization is an evolving technology, because it has the potential to facilitate the consolidation of multiple workloads on a single physical host directly through cloud. We introduce the First-Last algorithm to minimize energy consumption and migration. Methods: The problem is treated as an instance of bin packing algorithm which focuses on saving energy. However the overloading of active hosts and the resultant migration are the drawbacks of this approach. This algorithm was tested on a set of heterogeneous virtual machines which have demands in both single and multi-dimension and came either statically or dynamically to the server. Findings: The virtual machine placement problem can be treated as a bin packing problem, wherein the physical machines/hosts are the bins and the virtual machines are the objects. It can then be solved through various approaches such as first fit, best fit, next fit et cetera. Using the first fit algorithm, the virtual machine is placed into the first active host which can accommodate it. If none of the active hosts satisfy the VM's demand, then a new host is made active. In the best fit method, all the active hosts are checked first and then the host which would suffer minimum resource wastage upon VM placement is chosen. Next fit algorithm places the VM in the last/most recently activated host. While first fit is best suited for optimal performance, best fit keeps the least number of hosts active, thereby saving energy but at the cost of performance since migration overhead increases. Next fit approach would fail unless the virtual machines have been arranged in the increasing order of their demands. The firstlast algorithm that we introduce balances on energy consumption as well as performance by reducing migration. It also reduces the time complexity to a certain extent. Applications: The algorithm has a comparatively lower time complexity than the existing virtual machine placement algorithms. It also reduces the energy consumption as well as migration to a certain extent.</p>

Notes:

cited By 0

Cite this Research Publication

A. Kumar, Sathasivam, C., and Dr. Prakash P, “Virtual machine placement in cloud computing Environment”, Indian Journal of Science and Technology, vol. 9, no. 29, 2016.

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