Digital Twin-Driven Computing Resource Management for Vehicular Networks

TL;DR

This paper introduces a digital twin-based AI approach for dynamic computing resource management in vehicular networks, improving task support and scalability.

Contribution

It develops a two-tier digital twin framework and a two-stage resource allocation scheme tailored for vehicular network edge servers.

Findings

Supports more computing tasks than benchmarks

Adapts to network dynamics and vehicle mobility

Enhances scalability of resource management

Abstract

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.