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(VN),以支持各种智能城市应用。这些VN不断产生大量数据,需要严格的可靠性和安全性要求。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,如果没有得到适当隔离,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变得更加复杂。为了解决隔离问题,本文提出了隔离感知虚拟网络嵌入(VNE)的概念,即分配VN到物理基础设施的过程。我们定义了一个简单的隔离级别的抽象概念,以捕获隔离要求的变化,并将隔离感知VNE作为具有资源和隔离约束的优化问题进行了规定。提出了一个基于深度强化学习(DRL)的VNE算法ISO-DRL_VNE,它考虑了资源和隔离约束,并与现有的三种最先进的算法进行了比较:NodeRank、全局资源容量(GRC)和蒙特卡罗树搜索(MCTS)。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优于其他算法,分别提高了6%、13%和15%。