  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.
