Boost online virtual network embedding: Using neural networks for admission control
Boost online virtual network embedding: Using neural networks for admission control
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DOI：
10.1109/cnsm.2016.7818395
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发表时间：
2016-10
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影响因子：
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通讯作者：
Andreas Blenk;Patrick Kalmbach;Patrick van der Smagt;W. Kellerer
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文献类型：
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作者：
Andreas Blenk;Patrick Kalmbach;Patrick van der Smagt;W. Kellerer
物理资源到虚拟网络的分配，即，虚拟网络嵌入（VNE）由于其问题的复杂性，仍然是一个正在进行的研究领域。虽然在线VNE问题存在许多解决方案，但只有少数几个专注于通常可用于在线嵌入优化的方法。在本文中，我们提出了一个接纳控制的基础上，递归神经网络（RNN），以提高整体系统性能的在线VNE问题。在运行VNE算法以嵌入虚拟网络请求之前，RNN基于基底的当前状态和虚拟网络请求（VNR）来预测请求是否将被VNE算法接受。RNN防止VNE算法将时间花费在不可行或无法嵌入可接受时间的VNR上。为了有效地训练和操作RNN，我们还提出了底层网络和虚拟网络请求的新表示。该表示基于拓扑和网络资源特征，以低计算复杂度表示底层网络和VNR。通过模拟，我们表明，我们的准入控制减少了高达91%的在线VNE问题的整体计算时间，同时保持VNE的平均性能。使用我们新的基底和请求表示，RNN对于不同的VNE算法、基底尺寸和VNR到达率实现了89%到98%的准确度。
The allocation of physical resources to virtual networks, i.e., the virtual network embedding (VNE), is still an on-going research field due to its problem complexity. While many solutions for the online VNE problem exist, only few have focused on methods that can be generally applied for optimization of online embeddings. In this paper, we propose an admission control based on a Recurrent Neural Network (RNN) to improve the overall system performance for the online VNE problem. Before running a VNE algorithm to embed a virtual network request, the RNN predicts whether the request will be accepted by the VNE algorithm based on the current state of the substrate and the virtual network request (VNR). The RNN prevents VNE algorithms from spending time on VNRs that are either infeasible or that cannot be embedded in acceptable time. In order to train and operate the RNN efficiently, we additionally propose new representations for substrate networks and virtual network requests. The representations are based on topological and network resource features to represent the substrate network and the VNRs with low computational complexity. Via simulations, we show that our admission control reduces the overall computational time for the online VNE problem by up to 91 % while preserving VNE performance on average. Using our new substrate and request representations, the RNN achieves an accuracy ranging between 89 % and 98 % for different VNE algorithms, substrate sizes, and VNR arrival rates.