关键词: 网络功能虚拟化, 服务功能链部署, 无线资源分配, 强化学习, 深度确定性策略梯度算法

Abstract:

In the Network Function Virtualization（NFV） environment, it is important for operators to save resource consumption and reduce operating costs while ensuring the service quality of the users’ Service Function Chain（SFC）. This paper jointly considers SFC deployment and radio access network resource allocation, and proposes an SFC multi-dimensional resource allocation algorithm based on deep reinforcement learning. Firstly, an SFC resource allocation mechanism based on environment awareness is built, and an SFC deployment cost minimization model is established with the constraints of user delay requirements, wireless rate requirements and resource capacity. Secondly, considering the dynamics of the wireless environment, this optimization problem is transformed into a model-free discrete-time Markov Decision Process（MDP） model. Due to the continuity of the MDP’s state space and the high dimensionality of the action space, a Deep Deterministic Policy Gradient（DDPG） reinforcement learning algorithm is leveraged to solve the problem, accordingly a resource allocation strategy that minimizes the deployment cost is obtained. Simulation results show that the algorithm can effectively reduce the SFC deployment cost and end-to-end transmission delay while satisfying the constraints of performance requirements and resource capacity.

Key words: network function virtualization, service function chain deployment, radio resource allocation, reinforcement learning, deep deterministic policy gradient