Mendeley  

Zotero  

Mowahedi H. Review: Dynamic Resource Allocation in Edge Networks with a Digital Twin Approach. JOTS 2024; 2 (4) :114-128
URL: http://jots.knu.edu.af/article-1-36-en.html

Resource allocation in edge networks refers to the process of efficiently distributing computing, storage, and networking resources to meet the diverse needs of applications and services. With the emergence of Digital Twin (DT) technology—which provides virtual counterparts of physical objects—resource allocation in edge networks can be enhanced by leveraging the capabilities of digital twins. Digital twins offer real-time monitoring and analysis of physical assets, enabling a deeper understanding of their behavior and resource requirements. By integrating DT into edge networks, resource allocation can be optimized using insights gained from these virtual replicas. This integration enables dynamic resource allocation based on real-time data from DTs, facilitating predictive management and proactive resource assignment. This research aims to optimize resource allocation in dynamic and resource-constrained environments. The proposed method combines Deep Reinforcement Learning (DRL) with Digital Twin technology. Results show that this approach leads to reduced latency, improved energy efficiency, and enhanced resource allocation under varying conditions. As a result, integrating DT into edge networks improves resource optimization by providing realtime monitoring, predictive analysis, and intelligent decisionmaking capabilities.