论文标题
EdgeVision：迈向分布式边缘的协作视频分析以最大化性能
EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance Maximization
论文作者
论文摘要
基于深度神经网络（DNN）的视频分析可显着提高计算机视觉应用中的识别精度。在Edge节点上部署DNN模型，更接近最终用户，减少推理延迟并最大程度地减少带宽成本。但是，这些受资源受限的边缘节点可能会在大量工作量下经历大量延迟，从而导致工作负载分布不平衡。尽管以前的努力着重于优化层次结构 - 设备边缘云架构或用于视频分析的集中式群集，但我们建议通过协作分布式和自主边缘节点来应对这些挑战。尽管涉及复杂的控制，但我们还是引入了EdgeVision，这是一种多基因增强学习（MARL） - 基于分布式边缘的协作视频分析框架。 EdgeVision使Edge节点可以自主学习视频预处理，模型选择和请求调度的策略。我们的方法利用了一种基于参与者的MARL算法，并使用注意力机制来学习最佳策略。为了验证EdgeVision，我们使用现实世界数据集构建了一个多边测试床并进行实验。结果表明，与基线方法相比，性能提高了33.6％至86.4％。
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.
