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DOI: 10.14569/IJACSA.2016.071109
PDF

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Author 1: Alexander Ngenzi
Author 2: Selvarani R
Author 3: Suchithra R

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 11, 2016.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Live Migration is one of the essential operations that require more attention to addressing its high variability problems with virtual machines. We review the existing techniques of resource management to find that there are less modeling to solve this problem. The present paper introduces a novel framework that mainly targets to achieve a computational effective resource management technique. The technique uses the stochastic approach in modelling to design a new traffic management scheme that considers multiple traffic possibilities over VMs along with its switching states. Supported by an analytical modelling approach, the proposed technique offers an efficient placement of virtual machine to the physical server, performs the computation of blocks, and explores reduced resource usage. The study outcome was found to possess potential reduction in live migration, more extent of VM mapping with physical servers, and increased level of capacity.

Keywords: Resource Management; Live Migration; Virtual Machine; Load Balancing; Cloud Computing

Alexander Ngenzi, Selvarani R and Suchithra R, “Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing” International Journal of Advanced Computer Science and Applications(IJACSA), 7(11), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071109

@article{Ngenzi2016,
title = {Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071109},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071109},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {11},
author = {Alexander Ngenzi and Selvarani R and Suchithra R}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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