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Virtual Network Embedding in Ring Optical Data Centers Using Markov Chain Probability Model

机译:使用马尔可夫链概率模型将虚拟网络嵌入环形光数据中心

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Cloud data centers nowadays play an important role in providing computing and network resources for online applications and services. Such applications obtain cloud resources by submitting resource requests in the form of virtual networks that are embedded in the cloud infrastructures, referred to as virtual network embedding (VNE). Developing an effective VNE algorithm is crucial since it affects the performance of data centers, such as rejection ratio, resource utilization, and revenue. The problem is much more challenging when considering ring optical data centers due to multiple issues: wavelength continuity constraint, wavelength selection, and physical path selection for a lightpath. In this paper, we first develop an optimization programming formulation, which is computationally prohibitive. We then develop a novel VNE algorithm that adopts the Web page ranking approach to evaluate the goodness of a top-of-the-rack (ToR)-based on its resources in correlation with that of other ToRs. We also develop efficient methods for wavelength and physical path selections for a lightpath dynamically created during the embedding. We evaluate the proposed algorithm through comprehensive simulations in comparing with the optimal results and baseline algorithms. The simulation results show that the proposed algorithm performs close to the optimal one. It significantly reduces the rejection ratio by at least 23 compared to the baseline algorithms, leading to an increase in revenue by at least 14.
机译:如今,云数据中心在为在线应用程序和服务提供计算和网络资源方面发挥着重要作用。这样的应用程序通过以嵌入在云基础架构中的虚拟网络的形式提交资源请求来获得云资源,这称为虚拟网络嵌入(VNE)。开发有效的VNE算法至关重要,因为它会影响数据中心的性能,例如拒绝率,资源利用率和收入。在考虑环形光学数据中心时,由于以下多个问题,该问题更具挑战性:波长连续性约束,波长选择和光路的物理路径选择。在本文中,我们首先开发了一种优化编程公式,该公式在计算上是禁止的。然后,我们开发一种新颖的VNE算法,该算法采用网页排名方法来基于其与其他ToR的资源相关的资源来评估机架顶部(ToR)的优劣。我们还为嵌入过程中动态创建的光路开发了有效的波长和物理路径选择方法。通过与最佳结果和基准算法进行比较,我们通过综合仿真评估了提出的算法。仿真结果表明,该算法的性能接近最优算法。与基准算法相比,它可以将拒绝率至少降低23倍,从而使收入至少增加14倍。

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