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Deep Neural Network (DNN)-based video analytics significantly improvesrecognition accuracy in computer vision applications. Deploying DNN models atedge nodes, closer to end users, reduces inference delay and minimizesbandwidth costs. However, these resource-constrained edge nodes may experiencesubstantial delays under heavy workloads, leading to imbalanced workloaddistribution. While previous efforts focused on optimizing hierarchicaldevice-edge-cloud architectures or centralized clusters for video analytics, wepropose addressing these challenges through collaborative distributed andautonomous edge nodes. Despite the intricate control involved, we introduceEdgeVision, a Multiagent Reinforcement Learning (MARL)- based framework forcollaborative video analytics on distributed edges. EdgeVision enables edgenodes to autonomously learn policies for video preprocessing, model selection,and request dispatching. Our approach utilizes an actor-critic-based MARLalgorithm enhanced with an attention mechanism to learn optimal policies. Tovalidate EdgeVision, we construct a multi-edge testbed and conduct experimentswith real-world datasets. Results demonstrate a performance enhancement of33.6% to 86.4% compared to baseline methods.

EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance Maximization