Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Widespread adoption ofmobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus- a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted fromthe AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by similar to 4x and the energy consumption by similar to 77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.

Automated summary

To address the issues of computational burden, high energy consumption, and poor performance in mobile augmented reality (AR) and virtual reality (VR) applications, this paper introduces Nimbus - a task placement and offloading solution that offloads deep learning tasks from the AR application pipeline to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. The evaluation results show that Nimbus can significantly reduce task latency and energy consumption for real-time object detection in AR applications.