Resource and Fairness-Aware Digital Twin Service Caching and Request Routing With Edge Collaboration

Long Chen, Shaojie Zheng, Yalan Wu, Hong-Ning Dai, Jigang Wu

Research output: Contribution to journalJournal articlepeer-review


Intelligent edge services should consider not only service provider’s resource constraints but also end user’s quality of experience (QoE) demands. In this regard, we study a digital twin service caching and request routing problem with consideration of both resource and fairness-awareness under the collaboration of multi-access edge computing (MEC) servers. We first formulate the problem as an integer nonlinear programming by maximizing the minimum task completion ratio when there are multiple heterogeneous digital twin services, subject to computation, storage, and heterogeneous task delay constraints. Then, we propose an online stochastic randomized rounding algorithm (ORA) and an efficient online greedy algorithm (OGA) to solve the formulated problem. Extensive simulation results demonstrate the effectiveness of the proposed ORA and OGA algorithms in terms of the minimum application completion ratio.

Original languageEnglish
Number of pages5
JournalIEEE Wireless Communications Letters
Publication statusE-pub ahead of print - 24 Jul 2023

Scopus Subject Areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Fairness
  • Service model
  • Heterogeneous
  • Inference
  • Multi-access Edge Computing


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