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EE-CTA: Energy efficient, concurrent and topology-aware virtual network embedding as a multi-objective optimization problem

机译:EE-CTA:节能,并发和拓扑知识虚拟网络作为多目标优化问题嵌入

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Network virtualization provides the substruction of various heterogeneous Virtual Networks (VNs) on a single physical infrastructure which is called Virtual Network Embedding (VNE) and is known as an Np-hard problem. The VNE includes two sub-problem; virtual node mapping and virtual link mapping. Related works do not consider network topology and energy efficiency in the embedding process. This paper proposes Energy Efficient, Concurrent, and Topology-Aware (EE-CTA) algorithm as a new concurrent VNE method. Also, EE-CTA is energy-efficient due to using servers status and renewable energy resources when they are available. Our proposed EE-CTA has focused on network topology with assigning reachability rank to resources. In order to achieve all of these goals, we model VNE as a multi-objective optimization problem and solve it by Non-dominated Sorting Genetic Algorithm (NSGA-II). We compare EE-CTA with Presto, Ant Colony Optimization (ACO), Topology and Migration-Aware Energy Efficient (TMAE), and RW-Max match methods. The evaluation results demonstrate our method improves revenue, acceptance ratio, cost, and energy usage.
机译:网络虚拟化在单个物理基础架构上提供各种异构虚拟网络(VNS),该基础设施被称为虚拟网络嵌入(VNE),并且被称为NP难题。 VNE包括两个子问题;虚拟节点映射和虚拟链接映射。相关工程在嵌入过程中不考虑网络拓扑和能效。本文提出了作为新的并发VNE方法的节能,并发和拓扑信息(EE-CTA)算法。此外,EE-CTA是由于在可用时使用服务器状态和可再生能源的节能。我们所提出的EE-CTA专注于网络拓扑,并为资源分配可达性等级。为了实现所有这些目标,我们将VNE塑造为多目标优化问题,并通过非主导的分类遗传算法(NSGA-II)来解决它。我们将EE-CTA与PRESTO,蚁群优化(ACO),拓扑和迁移感知节能(TMAE)和RW-MAX匹配方法进行比较。评估结果表明我们的方法提高了收入,接受比率,成本和能源使用情况。

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