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Dynamic Human Digital Twin Deployment at the Edge for Task Execution: A Two-Timescale Accuracy-Aware Online Optimization

Yuye Yang, You Shi, Changyan Yi, Jun Cai, Jiawen Kang, Dusit Niyato, Xuemin, Shen

TL;DR

This work tackles HDT deployment at the network edge under mobility and status uncertainties by formulating a two‑timescale online optimization problem that jointly optimizes VT construction (generic placement and customized updates) and PT task offloading within an end‑edge‑cloud framework. It introduces TACO, a Lyapunov drift‑plus‑penalty–based approach that decomposes the long‑term problem into short‑term subproblems solved asynchronously on two timescales, using PME for large‑timescale decisions and BCD for small‑timescale decisions. The method provides convergence guarantees and analyzes computational complexity, demonstrating an asymptotically optimal performance gap bounded in closed form. Simulations on real mobility and ES distribution datasets show that TACO improves HDT‑assisted task execution accuracy while reducing service delay and system energy consumption, outperforming LOT and CRO baselines across various bandwidths, transmission rates, and numbers of PTs.

Abstract

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.

Dynamic Human Digital Twin Deployment at the Edge for Task Execution: A Two-Timescale Accuracy-Aware Online Optimization

TL;DR

This work tackles HDT deployment at the network edge under mobility and status uncertainties by formulating a two‑timescale online optimization problem that jointly optimizes VT construction (generic placement and customized updates) and PT task offloading within an end‑edge‑cloud framework. It introduces TACO, a Lyapunov drift‑plus‑penalty–based approach that decomposes the long‑term problem into short‑term subproblems solved asynchronously on two timescales, using PME for large‑timescale decisions and BCD for small‑timescale decisions. The method provides convergence guarantees and analyzes computational complexity, demonstrating an asymptotically optimal performance gap bounded in closed form. Simulations on real mobility and ES distribution datasets show that TACO improves HDT‑assisted task execution accuracy while reducing service delay and system energy consumption, outperforming LOT and CRO baselines across various bandwidths, transmission rates, and numbers of PTs.

Abstract

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.
Paper Structure (20 sections, 5 theorems, 64 equations, 10 figures, 2 tables, 2 algorithms)

This paper contains 20 sections, 5 theorems, 64 equations, 10 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Problem Formulation
  4. System Overview
  5. Generic VT Model Placement
  6. Customized VT Model Update
  7. HDT-Assisted Task Execution
  8. Problem Formulation
  9. A Two-Timescale Online Optimization Approach (TACO)
  10. Problem Reformulation
  11. Problem Decomposition
  12. Alternating Algorithm between Two Timescales
  13. Two-Timescale Decoupling and Alternation
  14. Solution for Large-Timescale Decisions
  15. Solution for Small-Timescale Decisions
  16. ...and 5 more sections

Key Result

Theorem 1

Let $V > 0$, and the drift-plus-penalty is bounded by any possible decisions in any time slot $\tau\in\mathcal{T}_t$, i.e., where is a positive constant that adjusts the tradeoff between the HDT-assisted task execution accuracy and the satisfaction degree of the delay and energy consumption constraints.

Figures (10)

  • Figure 1: The end-edge-cloud collaborative HDT system.
  • Figure 2: Two-timescale online optimization framework.
  • Figure 3: Flowchart of the proposed TACO approach.
  • Figure 4: Convergence of the proposed TACO approach.
  • Figure 5: Stability of two queue backlogs by varying $V$ and $K$.
  • ...and 5 more figures

Theorems & Definitions (5)

  • Theorem 1
  • Theorem 2
  • Theorem 3
  • Theorem 4
  • Theorem 5