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Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection

机译:基于最佳子图检测的虚拟网络嵌入算法

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One of the main objectives of cloud computing providers is increasing the revenue of their cloud datacenters by accommodating virtual network requests as many as possible. However, arrival and departure of virtual network requests fragment physical network’s resources and reduce the possibility of accepting more virtual network requests. To increase the number of virtual network requests accommodated by fragmented physical networks, we propose two virtual network embedding algorithms, which coarsen virtual networks using Heavy Edge Matching (HEM) technique and embed coarsened virtual networks on best-fit sub-substrate networks. The performance of the proposed algorithms are evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithms increase the acceptance ratio and the revenue.
机译:云计算提供商的主要目标之一是通过尽可能多地适应虚拟网络请求来增加其云数据中心的收入。但是,虚拟网络请求的到达和离开会分散物理网络的资源,并降低接受更多虚拟网络请求的可能性。为了增加由分散的物理网络容纳的虚拟网络请求的数量,我们提出了两种虚拟网络嵌入算法,它们使用重型边缘匹配(HEM)技术对虚拟网络进行粗化,然后将粗化的虚拟网络嵌入到最适合的子基板网络中。对所提算法的性能进行了评估,并与现有算法进行了广泛的仿真比较,结果表明所提算法提高了验收率和收益。

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