基于深度强化学习的能源高效VNF放置和链接方法

网络功能虚拟化(NFV)技术的出现使得网络功能由虚拟网络功能(VNF)提供, 从而提高网络的灵活性, 可扩展性和成本效益. 然而, NFV面临一个重要挑战是, 如何有效地将VNF放置不同的网络位置并链接起来引导流量, 同时最大限度减少能源消耗. 此外, 面对网络服务质量要求, 提高服务接受率对于网络性能也是至关重要的. 为了解决这些问题, 本文研究了NFV中的VNF放置和链接(VNFPC), 以最大化服务接受率同时权衡优化能源消耗. 因此, 在NFV中设计了一种基于Actor-Critic深度强化学习(DRL)的能源高效的VNFPC方法, 称为ACDRL-VNFPC. 该方法应用了适应性共享方案, 通过在多服务之间共享同类型VNF和多VNF共享同一个服务器来实现节能. 实验结果表明, 提出的算法有效权衡了能耗和服务接受率, 并且, 在执行时间方面也得到了优化. 与基准算法相比, ACDRL-VNFPC在服务接受率, 能耗和执行时间方面性能分别提高了2.39%, 14.93%和16.16%.

Energy Efficient VNF Placement and Chaining Approach Based on Deep Reinforcement Learning

The emergence of network function virtualization (NFV) technology allows network functions are provided by virtual network functions (VNFs) to improve network flexibility, scalability and cost-effectiveness. However, an important challenge for NFV is how to efficiently place VNFs in different network locations and chain them to steer traffic while minimizing energy consumption. In addition, in the face of network quality of service requirements, improving the service acceptance rate is also critical to network performance. To address these issues, in this study we investigate VNF placement and chaining (VNFPC) in NFV to maximize the service acceptance rate while optimizing the energy consumption trade-off. Therefore, an energy-efficient VNFPC method based on Actor-Critic deep reinforcement learning (DRL), called ACDRL-VNFPC, is designed in NFV. The approach applies adaptive sharing scheme to achieve energy savings by sharing the same type of VNFs among multiple services and sharing the same server among multiple VNFs. The experiment results show that the proposed algorithm effectively trades off the energy consumption and service acceptance rate, and the execution time is also optimized. Compared with the baseline algorithm, ACDRL-VNFPC improves the performance in terms of service acceptance rate, energy consumption and execution time by 2.39%, 14.93% and 16.16%, respectively.