基于人工智能的虚拟网络嵌入动态算法选择

IF 1.8 4区 计算机科学 Q3 TELECOMMUNICATIONS
Abdelmounaim Bouroudi, Abdelkader Outtagarts, Yassine Hadjadj-Aoul
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引用次数: 0

摘要

随着网络基础设施的日益复杂和异构化,对虚拟网络嵌入(VNE)的需求比以往任何时候都更加迫切。虚拟网络嵌入包括在物理基础设施上映射虚拟网络,以优化网络资源使用并提高整体网络性能。VNE 被认为是网络切片最重要的部分之一,但已被证明是一个 NP 难问题,没有精确的解决方案。为了解决这个问题,人们提出了一些启发式和元启发式方法。由于启发式方法不能提供令人满意的解决方案,元启发式方法可以很好地探索解决方案的空间,尽管它们需要测试多个解决方案,而这在现实环境中通常是不可行的。其他依靠深度强化学习(DRL)并与启发式相结合的方法性能更好,但不会暴露出问题,如停留在局部最小值或空间探索极限较低。不过,这些算法的性能因所采用的方法和要处理的问题而异,从而导致鲁棒性问题。为了克服这些限制,我们提出了一种基于算法选择范式的稳健放置方法。其主要思想是从一组学习策略中动态地选择最佳算法,即在每个时间步骤中的奖励和采样效率。所提出的策略就像一种元算法,能为网络带来更强的鲁棒性,因为它能为特定场景动态选择最佳解决方案。我们提出了两种选择算法。首先,我们考虑的是离线选择,即在选择期间外更新放置策略。在第二种算法中,不同的代理与选择过程同时更新,我们称之为在线选择。这两种解决方案都证明了它们的效率,并能根据部署服务的接受率动态选择最佳算法。此外,所提出的解决方案还能根据策略的优势,成功换算出最佳部署策略,同时优于所有独立算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A dynamic AI-based algorithm selection for Virtual Network Embedding

A dynamic AI-based algorithm selection for Virtual Network Embedding

With the increasing sophistication and heterogeneity of network infrastructures, the need for Virtual Network Embedding (VNE) is becoming more critical than ever. VNE consists of mapping virtual networks on top of the physical infrastructure to optimize network resource use and improve overall network performance. Considered as one of the most important bricks of network slicing, it has been proven to be an NP-hard problem with no exact solution. Several heuristics and meta-heuristics were proposed to solve it. As heuristics do not provide satisfactory solutions, meta-heuristics allow a good exploration of the solutions’ space, though they require testing several solutions, which is generally unfeasible in a real world environment. Other methods relying on deep reinforcement learning (DRL) and combined with heuristics yield better performance without revealing issues such as sticking at local minima or poor space exploration limits. Nevertheless, these algorithms present varied performances according to the employed approach and the problem to be treated, resulting in robustness problems. To overcome these limits, we propose a robust placement approach based on the Algorithm Selection paradigm. The main idea is to dynamically select the best algorithm from a set of learning strategies regarding reward and sample efficiency at each time step. The proposed strategy acts as a meta-algorithm that brings more robustness to the network since it dynamically selects the best solution for a specific scenario. We propose two selection algorithms. First, we consider an offline selection in which the placement strategies are updated outside the selection period. In the second algorithm, the different agents are updated simultaneously with the selection process, which we call an online selection. Both solutions proved their efficiency and managed to dynamically select the best algorithm regarding acceptance ratio of the deployed services. Besides, the proposed solutions succeed in commuting to the best placement strategy depending on the strategies’ strengths while outperforming all standalone algorithms.

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来源期刊
Annals of Telecommunications
Annals of Telecommunications 工程技术-电信学
CiteScore
5.20
自引率
5.30%
发文量
37
审稿时长
4.5 months
期刊介绍: Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.
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