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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This content will become publicly available on June 3, 2025
ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling
With faster wireless networks and server GPUs, offloading
high-accuracy but compute-intensive AR tasks implemented in Deep
Neural Networks (DNNs) to edge servers offers a promising way to
support high-QoE Augmented/Mixed Reality (AR/MR) applications. A
cost-effective way for AR app vendors to deploy such edge-assisted AR
apps to support a large user base is to use commercial
Machine-Learning-as-a-Service (MLaaS) deployed at the edge cloud. To
maximize cost-effectiveness, such an MLaaS provider faces a key design
challenge, \ie how to maximize the number of clients concurrently
served by each GPU server in its cluster while meeting per-client AR
task accuracy SLAs. The above AR offloading inference serving problem
differs from generic inference serving or video analytics serving in
one fundamental way: due to the use of local tracking which reuses the
last server-returned inference result to derive results for the
current frame, the offloading frequency and end-to-end latency of each
AR client directly affect its AR task accuracy (for all the frames).
In this paper, we present ARISE, a framework that optimizes the edge
server capacity in serving edge-assisted AR clients. Our design
exploits the intricate interplay between per-client offloading
schedule and batched inference on the server via proactively
coordinating offloading request streams from different AR clients.
Our evaluation using a large set of emulated AR clients and a 10-phone
testbed shows that \name supports 1.7x--6.9x more clients compared to
various baselines while keeping the per-client accuracy within the
client-specified accuracy SLAs.
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- NSF-PAR ID:
- 10530142
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705816
- Page Range / eLocation ID:
- 451 to 464
- Subject(s) / Keyword(s):
- Mobile Augmented Reality, Inference Serving, Machine-Learn-as-a-Service
- Format(s):
- Medium: X
- Location:
- Minato-ku, Tokyo Japan
- Sponsoring Org:
- National Science Foundation
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