Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks

TL;DR

This paper introduces a novel Digital Twin Network framework for IIoT that uses deep reinforcement learning to optimize stochastic computation offloading, significantly improving energy efficiency and data processing.

Contribution

It proposes a new DTN paradigm, formulates a stochastic offloading problem, and develops an AAC algorithm for optimal resource management in IIoT.

Findings

Significant energy efficiency improvements over benchmarks

Effective handling of stochastic task arrivals and resource heterogeneity

Demonstrated superiority of the AAC algorithm in simulations

Abstract

The rapid development of Industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this paper, we first propose a new paradigm Digital Twin Networks (DTN) to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present Asynchronous Actor-Critic (AAC) algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.