Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.03102 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913326490976256 |
|---|---|
| author | Gao, Guanyu Dong, Yuqi Wang, Ran Zhou, Xin |
| author_facet | Gao, Guanyu Dong, Yuqi Wang, Ran Zhou, Xin |
| contents | Deep Neural Network (DNN)-based video analytics significantly improves recognition accuracy in computer vision applications. Deploying DNN models at edge nodes, closer to end users, reduces inference delay and minimizes bandwidth costs. However, these resource-constrained edge nodes may experience substantial delays under heavy workloads, leading to imbalanced workload distribution. While previous efforts focused on optimizing hierarchical device-edge-cloud architectures or centralized clusters for video analytics, we propose addressing these challenges through collaborative distributed and autonomous edge nodes. Despite the intricate control involved, we introduce EdgeVision, a Multiagent Reinforcement Learning (MARL)- based framework for collaborative video analytics on distributed edges. EdgeVision enables edge nodes to autonomously learn policies for video preprocessing, model selection, and request dispatching. Our approach utilizes an actor-critic-based MARL algorithm enhanced with an attention mechanism to learn optimal policies. To validate EdgeVision, we construct a multi-edge testbed and conduct experiments with real-world datasets. Results demonstrate a performance enhancement of 33.6% to 86.4% compared to baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_03102 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance Maximization Gao, Guanyu Dong, Yuqi Wang, Ran Zhou, Xin Distributed, Parallel, and Cluster Computing Multiagent Systems Multimedia Deep Neural Network (DNN)-based video analytics significantly improves recognition accuracy in computer vision applications. Deploying DNN models at edge nodes, closer to end users, reduces inference delay and minimizes bandwidth costs. However, these resource-constrained edge nodes may experience substantial delays under heavy workloads, leading to imbalanced workload distribution. While previous efforts focused on optimizing hierarchical device-edge-cloud architectures or centralized clusters for video analytics, we propose addressing these challenges through collaborative distributed and autonomous edge nodes. Despite the intricate control involved, we introduce EdgeVision, a Multiagent Reinforcement Learning (MARL)- based framework for collaborative video analytics on distributed edges. EdgeVision enables edge nodes to autonomously learn policies for video preprocessing, model selection, and request dispatching. Our approach utilizes an actor-critic-based MARL algorithm enhanced with an attention mechanism to learn optimal policies. To validate EdgeVision, we construct a multi-edge testbed and conduct experiments with real-world datasets. Results demonstrate a performance enhancement of 33.6% to 86.4% compared to baseline methods. |
| title | EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance Maximization |
| topic | Distributed, Parallel, and Cluster Computing Multiagent Systems Multimedia |
| url | https://arxiv.org/abs/2211.03102 |