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DOI: 10.14569/IJACSA.2015.060118
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Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding

Author 1: Ashraf A. Shahin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 1, 2015.

  • Abstract and Keywords
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Abstract: Network virtualization allows cloud infrastructure providers to accommodate multiple virtual networks on a single physical network. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard). Effective virtual network embedding increases the revenue by increasing the number of accepted virtual networks. In this paper, we propose virtual network embedding algorithm, which improves virtual network embedding by coarsening virtual networks. Heavy Clique matching technique is used to coarsen virtual networks. Then, the coarsened virtual networks are enhanced by using a refined Kernighan-Lin algorithm. The performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing the acceptance ratio and the revenue.

Keywords: cloud computing; network virtualization; resource allocation; substrate network fragmentation; virtual network embedding; virtual network coarsening

Ashraf A. Shahin, “Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding” International Journal of Advanced Computer Science and Applications(IJACSA), 6(1), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060118

@article{Shahin2015,
title = {Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060118},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060118},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {1},
author = {Ashraf A. Shahin}
}



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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