  Mobile augmented reality (MAR) blends a real scenario with overlaid virtual
content, which has been envisioned as one of the ubiquitous interfaces to the
Metaverse. Due to the limited computing power and battery life of MAR devices,
it is common to offload the computation tasks to edge or cloud servers in close
proximity. However, existing offloading solutions developed for MAR tasks
suffer from high migration overhead, poor scalability, and short-sightedness
when applied in provisioning multi-user MAR services. To address these issues,
a MAR service-oriented task offloading scheme is designed and evaluated in
edge-cloud computing networks. Specifically, the task interdependency of MAR
applications is firstly analyzed and modeled by using directed acyclic graphs.
Then, we propose a look-ahead offloading scheme based on a modified Monte Carlo
tree (MMCT) search, which can run several multi-step executions in advance to
get an estimate of the long-term effect of immediate action. Experiment results
show that the proposed offloading scheme can effectively improve the quality of
service (QoS) in provisioning multi-user MAR services, compared to four
benchmark schemes. Furthermore, it is also shown that the proposed solution is
stable and suitable for applications in a highly volatile environment.
