Deterministic Delay of Digital-Twin-Assisted End-to-End Network Slicing in Industrial IoT via Multiagent Deep Reinforcement Learning
Lun Tang, Zhoulin Pu, Qiang Hou, Dongxu Fang, Qianbin Chen
Abstract
With the rapid development of the Internet of Things (IoT), many IoT devices are accessing the network. However, existing networks cannot fully meet the strict and diverse requirements for delay and reliability in delay-sensitive services. Dynamic changes in service requests and the states of service nodes cause a lack of guaranteed end-to-end (E2E) network slicing delay determinism. To address this issue, we propose a digital twin (DT)-assisted network slicing resource allocation scheme. By integrating DT and network slicing, we first construct a DT-assisted E2E network architecture, and construct the base and mapping models in the proposed architecture. Second, we use the stochastic network calculus (SNC) theory to analyze the E2E delay violation probability and characterize the relationship between delay and service reliability under given traffic arrival distributions and delay constraints. Then, we construct a joint resource allocation problem of time-frequency, computation, storage, and bandwidth resources to maximize the utility of the infrastructure provider while guaranteeing the deterministic delay. Furthermore, a multiagent deep reinforcement learning algorithm in a distributed architecture is used to solve the complex optimization problem, achieving efficient network resource allocation. Simulation results demonstrate that the proposed resource allocation scheme meets the requirements for deterministic delay and enhances system utility.