Distributionally Robust Optimization for Digital Twin Service Provisioning over Edge Computing
Yuxiang Li, Jiayuan Chen, Changyan Yi
TL;DR
The paper tackles robust provisioning of Digital Twin as a Service (DTaaS) over edge computing under uncertain future DT interaction requests, using AoI as the quality metric. It formulates a joint DT asset deployment and interaction-type selection problem and then develops a Wasserstein Distributionally Robust Optimization (WDRO) approach to handle distributional uncertainty, transforming the problem via multi-level Wasserstein dual transformations into a solvable mixed-integer program solved with Gurobi. Key contributions include the initial problem $ P_1$, its DRO reformulation $ P_2$, and the dual-transformed $ P_3$, enabling robust performance under unforeseen extreme request conditions. Simulation results show WDRO consistently outperforms practical baselines, achieving higher total utility gains and better resilience as network size grows or resource constraints tighten, demonstrating the practical viability of edge-enabled, AoI-aware DTaaS provisioning.
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.