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

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 and assesses the impact of offloading decisions over three distinct 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 successfully avoids exceeding temperature limits while keeping additional edge offloading to a minimum. These results also highlight how properly accounting for all features of wearables allows fully exploiting edge offloading opportunities.