Sustainable Placement with Cost Minimization in Wireless Digital Twin Networks

TL;DR

This paper addresses the challenge of sustainable digital twin placement in wireless networks by formulating a cost minimization problem that considers overload risks, transforming it into a solvable ILP, and proposing an efficient local search algorithm.

Contribution

It introduces a novel sustainability-aware DT placement model using chance-constrained ILP and develops an improved local search algorithm for efficient solutions.

Findings

Lower network deployment costs achieved.

Higher efficiency compared to previous schemes.

Effective handling of overload risks in DT placement.

Abstract

Digital twin (DT) technology has a high potential to satisfy different requirements of the ever-expanding new applications. Nonetheless, the DT placement in wireless digital twin networks (WDTNs) poses a significant challenge due to the conflict between unpredictable workloads and the limited capacity of edge servers. In other words, each edge server has a risk of overload when handling an excessive number of tasks or services. Overload risks can have detrimental effects on a network's sustainability, yet this aspect is often overlooked in the literature. In this paper, we aim to study the sustainability-aware DT placement problem for WDTNs from a cost minimization perspective. To this end, we formulate the DT placement-driven cost optimization problem as a chance-constrained integer programming problem. For tractability, we transform the original non-deterministic problem into a deterministic integer linear programming (ILP) problem using the sample average approximation (SAA) approach. We prove that the transformed problem remains NP-hard and thus finding a global optimal solution is very difficult. To strike a balance between time efficiency and performance guarantee, we propose an improved local search algorithm for this ILP by identifying high-quality starting states from historical search data and enhancing the search process. Numerical results show a lower cost and higher efficiency of our proposed method compared with the previous schemes.