Deep Neural Network (DNN) based video analytics empowers many computer vision-based applications to achieve high recognition accuracy. To reduce inference delay and bandwidth cost for video analytics, the DNN models can be deployed on the edge nodes, which are proximal to end users. However, the processing capacity of an edge node is limited, potentially incurring substantial delay if the inference requests on an edge node is overloaded. While efforts have been made to enhance video analytics by optimizing the configurations on a single edge node, we observe that multiple edge nodes can work collaboratively by utilizing the idle resources on each other to improve the overall processing capacity and resource utilization. To this end, we propose a Multiagent Reinforcement Learning (MARL) based approach, named as EdgeVision, for collaborative video analytics on distributed edges. The edge nodes can jointly learn the optimal policies for video preprocessing, model selection, and request dispatching by collaborating with each other to minimize the overall cost. We design an actor-critic-based MARL algorithm with an attention mechanism to learn the optimal policies. We build a multi-edge-node testbed and conduct experiments with real-world datasets to evaluate the performance of our method. The experimental results show our method can improve the overall rewards by 33.6%-86.4% compared with the most competitive baseline methods.
翻译:基于深神经网的视频分析仪(DNN)基于深神经网(DNN)的视频分析仪(DNN)使许多基于计算机的视觉应用程序能够实现高认知准确度。为减少视频分析仪的推论延迟和带宽成本,DNN模型可以部署在边缘节点上,这是对终端用户最接近的。然而,边缘节点的处理能力是有限的,如果边缘节点上的推论请求超负荷,则可能会造成重大延迟。虽然已经作出努力,通过优化单一边缘节点的配置来增强视频分析仪,我们观察到,多个边缘节点可以通过利用彼此的闲置资源开展协作,改善总体处理能力和资源利用。为此,我们建议采用多剂强化学习方法,称为Edge Vision,用于在分布边缘进行协作性视频分析。边缘节点可以共同学习视频预处理的最佳政策、模型选择的最佳政策,并通过彼此协作请求发送最佳的总体成本。我们设计了一个基于演员的MAL.6算法,同时设计了一个关注度最大的关注机制,以学习我们最佳的实验性实验方法。我们用最佳的实验性实验方法来改进整个实验性实验方法。我们的标准方法可以建立多种方法。我们用实验性实验方法来改进。