DDQN Based Slot Allocation for Flexible Ethernet

F1exible Ethernet (FlexE) is a new technique in transportation network which can provide more flexible resource management by network slicing and service isolation. With the increasing of service types, it is a challenging work to design a high efficiency multi-service resource allocation scheme to meet users’ different demands, such as latency, packet loss rate, etc. In this paper, we propose a new slot allocation scheme based on deep reinforcement learning (DRL) method to achieve FlexE multi-service oriented resource allocation by optimizing the queuing time and packet loss rate. This optimization problem is formulated as a Markov decision process (MDP) and it can be solved by double deep Q-network(DDQN) algorithm. Numerical results demonstrate that the proposed scheme outperforms the benchmark scheme in convergency, latency and packet loss rate.