基于深度强化学习的服务功能链映射算法.

This paper proposed an algorithm for SFC mapping based on deep reinforcement learning which was called DQNSFC, aiming at reducing the influence of SFC mapping on the average time delay and deployment failure ratio in the network. Firstly, it constructed a multi-layer NFV management and scheduling architecture to meet the requirements of resource awareness and equipment configuration of the algorithm. Secondly, based on Markov decision process, it formally described the SFC mapping problem. Finally, it constructed a deep reinforcement learning network, which used the average network delay and the operation expense of the deployment as reward and punishment feedback. After training, the target position of the virtual network function where to be deployed can be determined according to the network status. The simulation experiment verifies correctness and performance of this algorithm. Experiment shows that this algorithm can effectively reduce the average network delay and deployment failure ratio, and has certain advantages in algorithm running time. [ABSTRACT FROM AUTHOR]