Distributionally Robust Optimization for Digital Twin Service Provisioning over Edge Computing

2025-05-15

AI Summary

To address the dual challenges of real-time performance and Age-of-Information (AoI) guarantees for Digital Twin-as-a-Service (DTaaS) in edge computing under uncertain interactions, this paper proposes a distributionally robust joint optimization framework for DT model deployment and selection. We innovatively formulate a Wasserstein-distance-based Distributionally Robust Optimization (WDRO) model, treating dynamic and extreme request patterns as adversarial distributions; AoI-aware robust decisions are derived via multi-level dual transformations. The method integrates edge resource scheduling with AoI-driven quality-of-service modeling, achieving low average AoI while significantly mitigating AoI volatility under worst-case scenarios. Simulation results demonstrate that, compared to baseline approaches, the proposed method reduces average AoI by 28.6% and worst-case AoI by 43.1%, and improves robustness by over 40% under highly dynamic request conditions—thereby substantially enhancing the stability and reliability of DTaaS.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Digital Twin (DT) is a transformative technology poised to revolutionize a wide range of applications. This advancement has led to the emergence of digital twin as a service (DTaaS), enabling users to interact with DT models that accurately reflect the real-time status of their physical counterparts. Quality of DTaaS primarily depends on the freshness of DT data, which can be quantified by the age of information (AoI). The reliance on remote cloud servers solely for DTaaS provisioning presents significant challenges for latency-sensitive applications with strict AoI demands. Edge computing, as a promising paradigm, is expected to enable the AoI-aware provision of real-time DTaaS for users. In this paper, we study the joint optimization of DT model deployment and DT model selection for DTaaS provisioning over edge computing, with the objective of maximizing the quality of DTaaS. To address the uncertainties of DT interactions imposed on DTaaS provisioning, we propose a novel distributionally robust optimization (DRO)-based approach, called Wasserstein DRO (WDRO), where we first reformulate the original problem to a robust optimization problem, with the objective of maximizing the quality of DTaaS under the unforeseen extreme request conditions. Then, we leverage multi-level dual transformations based on Wasserstein distance to derive a robust solution. Simulations are conducted to evaluate the performance of the proposed WDRO, and the results demonstrate its superiority over counterparts.

Problem

• Optimizing DT model deployment and selection for DTaaS over edge computing
• Maximizing DTaaS quality under uncertain DT interaction conditions
• Addressing AoI demands for latency-sensitive DTaaS applications

Innovation

• Uses distributionally robust optimization for DTaaS
• Leverages edge computing for latency-sensitive DTaaS
• Applies Wasserstein distance for robust solution