Load balance and time delay efficient algorithm for VNF service chain deployment based on DQN

In mobile edge computing, computing and storage resources are close to the network of the user device, which can effectively reduce network access and computing service delay. However, network function virtualization for mobile edge computing networks is more challenging due to the strong coupling of CPU resources, storage resources, and wireless resources. In order to solve the problems of load balance and long response time of user-oriented service function chain deployment algorithm in mobile edge computing networks, this paper proposed a load balance and time delay efficient algorithm (denoted as LBDQN) for virtual network function service chain deployment problem based Deep Q-LearningFirst, a global optimization model, which minimizes time delay and maximizes the load balance, is established. Then, a new real-time algorithm for VNF service chain deployment based on DQN is proposed. In this algorithm, an adaptive ε-greedy method is designed. In addition, a particle swarm optimization is used to optimize the neural network parameters. To demonstrate the efficiency of the proposed algorithm, some simulation experiments are conducted and the experimental results show that the algorithm can effectively reduce the time delay and improve the load balance than the compared algorithms.