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.
Highlights
In cloud computing data centers, virtualization is employed to accommodate multiple virtual networks (VNs) on a single substrate network (SN), and multiple virtual servers on a single physical server [1]
Effective VN coarsening can improve the utilization of SN’s resources and increase the acceptance ratio of VNs and the revenue of infrastructure providers, most of current virtual network embedding algorithms do not take into account VN coarsening [2, 3, 4, 5, 6, 7]
We propose virtual network embedding algorithm, which coarsens virtual networks using Heavy Clique matching technique
Summary
In cloud computing data centers, virtualization is employed to accommodate multiple virtual networks (VNs) on a single substrate network (SN), and multiple virtual servers on a single physical server [1]. Effective VN coarsening can improve the utilization of SN’s resources and increase the acceptance ratio of VNs and the revenue of infrastructure providers, most of current virtual network embedding algorithms do not take into account VN coarsening [2, 3, 4, 5, 6, 7]. We propose virtual network embedding algorithm, which coarsens virtual networks using Heavy Clique matching technique. 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.
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