Adaptive Edge Offloading for Image Classification Under Rate Limit
- URL: http://arxiv.org/abs/2208.00485v1
- Date: Sun, 31 Jul 2022 18:06:33 GMT
- Title: Adaptive Edge Offloading for Image Classification Under Rate Limit
- Authors: Jiaming Qiu, Ruiqi Wang, Ayan Chakrabarti, Roch Guerin, Chenyang Lu
- Abstract summary: The paper develops a policy based on a Deep Q-Network (DQN), and demonstrates both its efficacy and the feasibility of its deployment on embedded devices.
The evaluation is carried out by performing image classification over a local testbed using synthetic traces generated from the ImageNet image classification benchmark.
- Score: 18.029207345709413
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper considers a setting where embedded devices are used to acquire and
classify images. Because of limited computing capacity, embedded devices rely
on a parsimonious classification model with uneven accuracy. When local
classification is deemed inaccurate, devices can decide to offload the image to
an edge server with a more accurate but resource-intensive model. Resource
constraints, e.g., network bandwidth, however, require regulating such
transmissions to avoid congestion and high latency. The paper investigates this
offloading problem when transmissions regulation is through a token bucket, a
mechanism commonly used for such purposes. The goal is to devise a lightweight,
online offloading policy that optimizes an application-specific metric (e.g.,
classification accuracy) under the constraints of the token bucket. The paper
develops a policy based on a Deep Q-Network (DQN), and demonstrates both its
efficacy and the feasibility of its deployment on embedded devices. Of note is
the fact that the policy can handle complex input patterns, including
correlation in image arrivals and classification accuracy. The evaluation is
carried out by performing image classification over a local testbed using
synthetic traces generated from the ImageNet image classification benchmark.
Implementation of this work is available at
https://github.com/qiujiaming315/edgeml-dqn.
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