论文标题
通过深度加强学习嵌入孤立感知的在线虚拟网络
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning
论文作者
论文摘要
虚拟化技术是现代ICT基础架构的基础，使服务提供商能够创建专用的虚拟网络（VNS），可以支持广泛的智能城市应用程序。这些VN不断生成大量数据，需要严格的可靠性和安全要求。但是，在虚拟化的网络环境中，多个VN可以在相同的物理基础架构上共存，如果不正确隔离，可能会干扰或提供未经授权的访问权限。前者会导致性能退化，而后者损害了VN的安全性。当特定的VN违反隔离要求时，基础设施提供商的服务保证变得更加复杂。 为了解决隔离问题，本文提议在虚拟网络嵌入（VNE）期间隔离，将VNS分配到物理基础架构上的过程。我们定义了一个简单的隔离级别概念，以捕获隔离要求中的变化，然后将隔离感与vne提出，作为资源和隔离约束的优化问题。提出了深入的强化学习（DRL）基于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进行了比较：Noderank：Noderank，全球资源能力（GRC）和Mote-Carlo Tree搜索（MCTS）。评估结果表明，ISO-DRL_VNE算法以接受比，长期平均收入和长期平均收入比率比其他人的表现优于其他人，增长了6％，13％和15％。
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%.
