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%.

翻译：虚拟化技术是现代信息通信基础设施的基石，使服务提供商能够创建支持广泛智慧城市应用的专用虚拟网络（VN）。这些VN持续生成海量数据，对可靠性和安全性提出了严格要求。然而，在虚拟化网络环境中，多个VN可能共存于同一物理基础设施上，若未实现适当隔离，可能相互干扰或导致未授权访问。前者会造成性能下降，后者则会危及VN的安全性。当特定VN违反隔离要求时，基础设施提供商的服务保障将变得异常复杂。为解决隔离问题，本文提出在虚拟网络映射（VNE）——即VN至物理基础设施的分配流程中——实现隔离。我们定义了简洁抽象的隔离层级概念以捕捉隔离需求的差异，随后将感知隔离的VNE形式化为具有资源与隔离约束的优化问题。提出一种基于深度强化学习（DRL）的VNE算法ISO-DRL_VNE，该算法同时考虑资源与隔离约束，并与现有三种先进算法——NodeRank、全局资源容量（GRC）和蒙特卡洛树搜索（MCTS）——进行对比。评估结果表明，ISO-DRL_VNE算法在接受率、长期平均收益和长期平均收益成本比方面分别优于对比算法6%、13%和15%。