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A batch approach for a survivable virtual network embedding based on Monte-Carlo Tree Search

机译：基于蒙特卡洛树搜索的可生存虚拟网络嵌入的批处理方法

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In this paper, we address the survivable batch-embedding virtual network problem within Cloud's backbone. In fact, the batch mapping of virtual networks will enhance the cumulative Cloud provider's revenue thanks to the global view of the incoming requests during a predefined time slot. Hence, the differentiation between requests can be performed and the arrival order of requests is ignored. The embedding of one virtual network is NP-hard. Adding the batch processing of the requests will further increase the complexity of the problem. In order to skirt the exponential complexity, we formulate the problem as building and researching problems within a decision tree. To resolve it, we propose a novel reliable batch-embedding virtual network strategy denoted by BR-VNE. It is based on Monte-Carlo Tree Search optimization method in which the upper confidence bounds can be reached in polynomial time. Based on extensive simulations, the results obtained show that BR-VNE outperforms the related work in terms of i) acceptance rate of virtual network requests, ii) Cloud provider's revenue and iii) rate of requests impacted by physical failures within the Cloud's backbone.
机译：在本文中，我们解决了Cloud骨干网中可生存的批量嵌入虚拟网络问题。实际上，由于在预定义的时间段内对传入请求进行了全局查看，因此虚拟网络的批量映射将提高累积的云提供商的收入。因此，可以执行请求之间的区分，并且忽略请求的到达顺序。一个虚拟网络的嵌入是NP困难的。添加请求的批处理将进一步增加问题的复杂性。为了避免指数级复杂性，我们将问题表述为在决策树中构建和研究问题。为了解决这个问题，我们提出了一种由BR-VNE表示的新颖可靠的批量嵌入虚拟网络策略。它基于蒙特卡洛树搜索优化方法，可以在多项式时间内达到置信上限。基于广泛的模拟，获得的结果表明BR-VNE在以下方面优于相关工作：i）虚拟网络请求的接受率，ii）云提供商的收入以及iii）受云主干网中物理故障影响的请求率。

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