Delay-Sensitive Computation Partitioning for Mobile Augmented Reality Applications

Chaokun Zhang, Rong Zheng, Yong Cui, Chenhe Li, Jianping Wu
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引用次数: 5

Abstract

Good user experiences in Mobile Augmented Reality (MAR) applications require timely processing and rendering of virtual objects on user devices. Today's wearable AR devices are limited in computation, storage, and battery lifetime. Edge computing, where edge devices are employed to offload part or all computation tasks, allows an acceleration of computation without incurring excessive network latency. In this paper, we use acyclic data flow graphs to model the computation and data flow in MAR applications and aim to minimize the makespan of processing input frames. Due to task dependencies and variable resource availability, makespan minimization is proven to be NP-hard in general. We design DPA, a polynomial-time heuristic algorithm for this problem. For special data flow graphs including chain or star, the algorithm can provide optimal solutions or solutions with a constant approximation ratio. The effectiveness of DPA has been evaluated using extensive simulations with realistic workloads and resource availability measured from a prototype implementation.
移动增强现实应用的延迟敏感计算分区
在移动增强现实(MAR)应用中,良好的用户体验需要及时处理和渲染用户设备上的虚拟对象。如今的可穿戴AR设备在计算、存储和电池寿命方面受到限制。边缘计算,其中边缘设备被用来卸载部分或全部的计算任务,允许加速计算而不会产生过多的网络延迟。在本文中,我们使用非循环数据流图来建模MAR应用中的计算和数据流,目的是最小化处理输入帧的最大时间跨度。由于任务依赖性和可变的资源可用性,最大时间跨度最小化通常被证明是np困难的。针对这一问题,我们设计了一个多项式时间启发式算法DPA。对于包括链或星型在内的特殊数据流图,该算法可以提供最优解或具有常数近似比的解。DPA的有效性已经通过广泛的模拟进行了评估,这些模拟具有现实的工作负载和从原型实现中测量的资源可用性。
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