Network Function Virtualization (NFV) and service orchestration simplify the deployment and management of network and telecommunication services. The deployment of these services requires, typically, the allocation of Virtual Network Function - Forwarding Graph (VNF-FG), which implies not only the fulfillment of the service’s requirements in terms of Quality of Service (QoS), but also considering the constraints of the underlying infrastructure. This topic has been well-studied in existing literature, however, its complexity and uncertainty of available information unveil challenges for researchers and engineers. In this paper, we explore the potential of reinforcement learning techniques for the placement of VNF-FGs. However, it turns out that even the most well-known learning technique is ineffective in the context of a large-scale action space. In this respect, we propose approaches to find out feasible solutions while improving significantly the exploration of the action space. The simulation results clearly show the effectiveness of the proposed learning approach for this category of problems. Moreover, thanks to the deep learning process, the performance of the proposed approach is improved over time.

中文翻译：

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VNF转发图嵌入的深度强化学习方法

网络功能虚拟化 (NFV) 和服务编排简化了网络和电信服务的部署和管理。这些服务的部署通常需要分配虚拟网络功能 - 转发图 (VNF-FG)，这意味着不仅要满足服务在服务质量 (QoS) 方面的要求，还要考虑底层基础设施。该主题已在现有文献中得到充分研究，但是，其可用信息的复杂性和不确定性为研究人员和工程师带来了挑战。在本文中，我们探讨了强化学习技术在放置 VNF-FG 方面的潜力。然而，事实证明，即使是最著名的学习技术在大规模行动空间的背景下也是无效的。在这方面，我们提出了寻找可行解决方案的方法，同时显着改善了对动作空间的探索。模拟结果清楚地表明了所提出的学习方法对此类问题的有效性。此外，由于深度学习过程，所提出的方法的性能随着时间的推移而提高。