Resource allocation for network slicing in dynamic multi-tenant networks: A deep reinforcement learning approach

Abstract

To support the wide range of 5G use cases in a cost-efficient way, network slicing has been considered a promising solution, which makes it possible to serve multiple customized and isolated network services on a common physical infrastructure. In this paper, we investigate the resource allocation problem of network slicing in multi-tenant networks where network resources can be used by low-priority tenants change dynamically due to the preemption of high-priority tenants. We formulate the problem as an energy-minimizing mathematical optimization problem considering practical constraints. Due to the dynamic characteristics of the problem, the complexity of the optimization problem is exceptionally high, making it impossible to solve the problem in real-time using traditional optimization approaches. With discovering the special structure of the problem, we propose a Dueling-Deep Q Network (DQN)-based algorithm to solve the problem efficiently. The experimental results show that the proposed algorithm outperforms compared algorithms in terms of total energy cost, runtime, and robustness.