Improved double deep Q network algorithm for service function chain deployment
Network Function Virtualization(NFV)has become the key technology of next generation communication.Virtual Network Function Service Chain(VNF-SC)mapping is the key issue of the NFV.To reduce the energy consumption of the communication network server and improve the quality of service,a Function Chain(SFC)deployment algorithm based on an improved Double Deep Q Network(DDQN)is proposed to reduce the energy consumption of network servers and improve the network quality of service.Due to the dynamic change of the network state,the service function chain deployment problem is modeled as a Markov Decision Process(MDP).Based on the network state and action rewards,the DDQN is trained online to obtain the optimal deployment strategy for the service function chain.To solve the problem that traditional deep reinforcement learning draws experience samples uniformly from the experience replay pool leading to low learning efficiency of the neural network,a prioritized experience replay method based on importance sampling is designed to draw experience samples so as to avoid high correlation between training samples to improve the learning efficiency of the neural network.Experimental results show that the proposed SFC deployment algorithm based on the improved DDQN can increase the reward value,and that compared with the traditional DDQN algorithm,it can reduce the energy consumption and blocking rate by 19.89％～36.99％and 9.52％～16.37％,respectively.
service function chainMarkov decision processnetwork energy consumptionDDQN
万方数据
维普
