# Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing

arXiv: https://arxiv.org/abs/2305.19558 · HTML: https://mlsanity.com/paper/2305.19558/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 5 of 5)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading](https://mlsanity.com/paper/1708.08810/) (2017)
- [MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems](https://mlsanity.com/paper/2112.07269/) (2021)
- [Path Planning for the Dynamic UAV-Aided Wireless Systems using Monte Carlo Tree Search](https://mlsanity.com/paper/2202.02457/) (2022)

## Built on by (most cited first, 3 of 3)

- [Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization](https://mlsanity.com/paper/2408.03631/) (2024)
- [Emergent communication for AR](https://mlsanity.com/paper/2308.07342/) (2023)
- [Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels](https://mlsanity.com/paper/2406.16878/) (2024)

## Abstract (arXiv)

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.