Distributed Deep Reinforcement Learning based Network Slice Handoff Algorithm for Tasks with Finite Buffer Model

Network slicing technology can provide customized services to many emerging applications with different network requirements, and it is playing an important role in various fields. However, most of the existing research work on handoff problems of network slices use the full buffer task model which can not be used to evaluate the performance of latency. Given latency is an important metric for many emerging applications, the network slice handoff problem using the finite buffer task model is studied in this paper. Besides, considering that the increase of number of users and applications leads to the increasingly complicated state of the network, the network slice handoff process is modeled as a Decentralized Markov Decision Process (DEC-MDP), and a network slice handoff algorithm based on distributed deep reinforcement learning is proposed. Simulation results prove that the algorithm proposed in this paper can significantly reduce the decision complexity while maintaining a relatively superior overall system performance.