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.

翻译：基于深度神经网络（DNN）的视频分析技术显著提升了计算机视觉应用的识别精度。将DNN模型部署在更接近终端用户的边缘节点，可降低推理延迟并最小化带宽成本。然而，这些资源受限的边缘节点在重负载下可能产生显著延迟，导致工作负载分布失衡。尽管先前研究聚焦于优化分层设备-边缘-云架构或集中式集群的视频分析方案，我们提出通过协作式分布式自主边缘节点来应对这些挑战。针对其中涉及的复杂控制问题，我们提出EdgeVision——一种基于多智能体强化学习（MARL）的分布式边缘视频分析协作框架。EdgeVision使边缘节点能够自主学习视频预处理、模型选择及请求分发的策略。本方法采用基于注意力机制增强的Actor-Critic型MARL算法来学习最优策略。为验证EdgeVision，我们构建了多边缘测试平台，并基于真实数据集开展实验。结果表明，与基线方法相比，性能提升幅度达33.6%至86.4%。