An Isolation-aware Online Virtual Network Embedding via Deep Reinforcement Learning

Ali Gohar, Chunming Rong, Sanghwan Lee
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Abstract

Virtualization technologies are the foundation of modern ICT infrastructure, enabling service providers to create dedicated virtual networks (VNs) that can support a wide range of smart city applications. These VNs continuously generate massive amounts of data, necessitating stringent reliability and security requirements. In virtualized network environments, however, multiple VNs may coexist on the same physical infrastructure and, if not properly isolated, may interfere with or provide unauthorized access to one another. The former causes performance degradation, while the latter compromises the security of VNs. Service assurance for infrastructure providers becomes significantly more complicated when a specific VN violates the isolation requirement. In an effort to address the isolation issue, this paper proposes isolation during virtual network embedding (VNE), the procedure of allocating VNs onto physical infrastructure. We define a simple abstracted concept of isolation levels to capture the variations in isolation requirements and then formulate isolation-aware VNE as an optimization problem with resource and isolation constraints. A deep reinforcement learning (DRL)-based VNE algorithm ISO-DRL VNE, is proposed that considers resource and isolation constraints and is compared to the existing three state-of-the-art algorithms: NodeRank, Global Resource Capacity (GRC), and Mote-Carlo Tree Search (MCTS). Evaluation results show that the ISO-DRL VNE algorithm outperforms others in acceptance ratio, long-term average revenue, and long-term average revenue-to-cost ratio by 6%, 13%, and 15%.
基于深度强化学习的隔离感知在线虚拟网络嵌入
虚拟化技术是现代ICT基础设施的基础,使服务提供商能够创建专用的虚拟网络(VNs),以支持广泛的智慧城市应用。这些vpn不断产生大量的数据,对可靠性和安全性提出了严格的要求。然而,在虚拟化网络环境中,多个vpn可能共存于同一物理基础设施上,如果隔离不当,可能会相互干扰或提供未经授权的访问。前者会导致性能下降,而后者会降低vpn的安全性。当特定的VN违反隔离需求时,基础设施提供者的服务保证会变得非常复杂。为了解决隔离问题,本文提出了虚拟网络嵌入(VNE)过程中的隔离,即在物理基础设施上分配虚拟网络的过程。我们定义了隔离级别的简单抽象概念,以捕获隔离需求的变化,然后将隔离感知的VNE表述为具有资源和隔离约束的优化问题。提出了一种基于深度强化学习(DRL)的VNE算法ISO-DRL VNE,该算法考虑了资源和隔离约束,并与现有的三种最先进的算法:NodeRank、Global resource Capacity (GRC)和Mote-Carlo Tree Search (MCTS)进行了比较。评估结果表明,ISO-DRL VNE算法在接受率、长期平均收入和长期平均收入成本比上分别优于其他算法6%、13%和15%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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