用强化学习动态调度XR任务，省电163%还保低延迟
Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines
- 基于轻量级深度强化学习在线决策执行位置
- 电池续航提升163%，延迟合规率超90%
- 适合资源受限的沉浸式XR系统部署
沉浸式扩展现实（XR）应用带来严苛的低延迟任务，在能量与电池受限设备上运行时，需在终端与边缘服务器间合理分配计算负载。现有自适应执行与卸载方法多优化平均性能，未能充分捕捉实时延迟要求与设备电池寿命之间的闭环交互。本文提出一种面向边缘辅助XR系统的电池感知执行管理框架，联合考虑执行放置、工作负载质量、延迟需求及电池动态变化。设计了一种基于轻量级深度强化学习策略的在线决策机制，在动态网络条件下持续调整执行决策，保持高运动到光子延迟合规性。实验表明，相比仅优化延迟的本地执行，该方法在稳定网络下可将设备电池寿命延长高达163%，延迟合规率维持在90%以上；即使在网络带宽严重受限时，合规率仍不低于80%，证明了显式管理延迟-能耗权衡在沉浸式XR系统中的有效性。
原文摘要 · Abstract (English)
Immersive extended reality (XR) applications introduce latency-critical workloads that must satisfy stringent real-time responsiveness while operating on energy- and battery-constrained devices, making execution placement between end devices and nearby edge servers a fundamental systems challenge. Existing approaches to adaptive execution and computation offloading typically optimize average performance metrics and do not fully capture the sustained interaction between real-time latency requirements and device battery lifetime in closed-loop XR workloads. In this paper, we present a battery-aware execution management framework for edge-assisted XR systems that jointly considers execution placement, workload quality, latency requirements, and battery dynamics. We design an online decision mechanism based on a lightweight deep reinforcement learning policy that continuously adapts execution decisions under dynamic network conditions while maintaining high motion-to-photon latency compliance. Experimental results show that the proposed approach extends the projected device battery lifetime by up to 163% compared to latency-optimal local execution while maintaining over 90% motion-to-photon latency compliance under stable network conditions. Such compliance does not fall below 80% even under significantly limited network bandwidth availability, thereby demonstrating the effectiveness of explicitly managing latency-energy trade-offs in immersive XR systems.
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