Edge-assisted AR supports high-quality AR on resource-constrained mobile devices by offloading high-rate camera-captured frames to powerful GPU edge servers to perform heavy vision tasks. Since the result of an offloaded frame may not come back in the same frame interval, edge-assisted AR designs resort to local tracking on the last server returned result to generate more accurate result for the current frame. In such an offloading+local tracking paradigm, reducing the staleness of the last server returned result is critical to improving AR task accuracy.
In this paper, we present MPCP, an online offloading scheduling framework that minimizes the staleness of server-returned result in edge-assisted AR by optimally pipelining network transfer of frames to the edge server and the Deep Neural Network inference on the edge server. MPCP is based on model predictive control (MPC). Our evaluation results show that MPCP reduces the depth estimation error by up to 10.0% compared to several baseline schemes.
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Do Larger (More Accurate) Deep Neural Network Models Help in Edge-assisted Augmented Reality?
Edge-assisted Augmented Reality (AR) which offloads computeintensive
Deep Neural Network (DNN)-based AR tasks to edge
servers faces an important design challenge: how to pick the DNN
model out of many choices proposed for each AR task for offloading.
For each AR task, e.g., depth estimation, many DNN-based models
have been proposed over time that vary in accuracy and complexity.
In general, more accurate models are also more complex; they are
larger and have longer inference time. Thus choosing a larger model
in offloading can provide higher accuracy for the offloaded frames
but also incur longer turnaround time, during which the AR app
has to reuse the estimation result from the last offloaded frame,
which can lead to lower average accuracy.
In this paper, we experimentally study this design tradeoff using
depth estimation as a case study. We design optimal offloading
schedule and further consider the impact of numerous factors such
as on-device fast tracking, frame downsizing and available network
bandwidth. Our results show that for edge-assisted monocular
depth estimation, with proper frame downsizing and fast tracking,
compared to small models, the improved accuracy of large models
can offset its longer turnaround time to provide higher average estimation
accuracy across frames under both LTE and 5G mmWave.
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- Award ID(s):
- 2112778
- NSF-PAR ID:
- 10342975
- Date Published:
- Journal Name:
- NAI'21: Proceedings of the ACM SIGCOMM 2021 Workshop on Network-Application Integration
- Page Range / eLocation ID:
- 47 to 52
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3472727.3472807
