# XR Offloading Across Multiple Time Scales: The Roles of Power, Temperature, and Energy

arXiv: https://arxiv.org/abs/2506.18584 · HTML: https://mlsanity.com/paper/2506.18584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [HybridInfer: Thermal-Aware Reinforcement-Learning Tier Routing for On-Device, Edge, and Cloud LLM Inference](https://mlsanity.com/paper/2609.30270/) (2026)

## Abstract (arXiv)

Extended reality (XR) devices, commonly known as wearables, must handle significant computational loads under tight latency constraints. To meet these demands, they rely on a combination of on-device processing and edge offloading. This letter focuses on offloading strategies for wearables by considering their impact across three time scales: instantaneous power consumption, short-term temperature fluctuations, and long-term battery duration. We introduce a comprehensive system model that captures these temporal dynamics, and propose a stochastic and stationary offloading strategy, called TAO (for temperature-aware offloading), designed to minimize the offloading cost while adhering to power, thermal, and energy constraints. Our performance evaluation, leveraging COMSOL models of real-world wearables, confirms that TAO reduces offloading cost by over 35% compared to state-of-the-art approaches, without violating the wearable operational limits.