IEEE Transactions on Cloud Computing (IEEE TCC) 
2022

Impact Factor: 5.938

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

Abstract:

Widespread adoption of mobile 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 from the 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 ~4X 
and the energy consumption by ~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.


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BibTeX:
@article{Cozzolino:TCC22,
title = "Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services",
journal = "IEEE Transactions on Cloud Computing",
year = "2022",
author = "Vittorio Cozzolino, Leonardo Tonetto, Nitinder Mohan, Aaron Yi Ding, Joerg Ott",
}
How to cite:

Vittorio Cozzolino, Leonardo Tonetto, Nitinder Mohan, Aaron Yi Ding, Joerg Ott. "Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services", in IEEE Transactions on Cloud Computing, 2022.