Delay-Sensitive Computation Partitioning for Mobile Augmented Reality Applications

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.