School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2021 Apr 13;21(8):2733. doi: 10.3390/s21082733.
Network function virtualization (NFV) is a key technology to decouple hardware device and software function. Several virtual network functions (VNFs) combine into a function sequence in a certain order, that is defined as service function chain (SFC). A significant challenge is guaranteeing reliability. First, deployment server is selected to place VNF, then, backup server is determined to place the VNF as a backup which is running when deployment server is failed. Moreover, how to determine the accurate locations dynamically with machine learning is challenging. This paper focuses on resource requirements of SFC to measure its priority meanwhile calculates node priority by current resource capacity and node degree, then, a novel priority-awareness deep reinforcement learning (PA-DRL) algorithm is proposed to implement reliable SFC dynamically. PA-DRL determines the backup scheme of each VNF, then, the model jointly utilizes delay, load balancing of network as feedback factors to optimize the quality of service. In the experimental results, resource efficient utilization, survival rate, and load balancing of PA-DRL were improved by 36.7%, 35.1%, and 78.9% on average compared with benchmark algorithm respectively, average delay was reduced by 14.9%. Therefore, PA-DRL can effectively improve reliability and optimization targets compared with other benchmark methods.
网络功能虚拟化（NFV）是解耦硬件设备和软件功能的关键技术。几个虚拟网络功能（VNF）按特定顺序组合成一个功能序列，这被定义为服务功能链（SFC）。一个显著的挑战是保证可靠性。首先，选择部署服务器来放置 VNF，然后确定备份服务器来放置 VNF 作为部署服务器故障时运行的备份。此外，如何通过机器学习确定准确的位置是具有挑战性的。本文专注于 SFC 的资源需求来衡量其优先级，同时通过当前资源容量和节点度来计算节点优先级，然后提出了一种新颖的基于优先级感知的深度强化学习（PA-DRL）算法来动态实现可靠的 SFC。PA-DRL 确定每个 VNF 的备份方案，然后，该模型联合利用延迟、网络负载平衡作为反馈因素来优化服务质量。在实验结果中，与基准算法相比，PA-DRL 在资源利用率、存活率和负载均衡方面分别平均提高了 36.7%、35.1%和 78.9%，平均延迟降低了 14.9%。因此，PA-DRL 可以有效地提高可靠性和优化目标，与其他基准方法相比。
