LOS：网状边缘网络传感器数据流异常检测周期模型训练的局部乐观调度
LOS: Local-Optimistic Scheduling of Periodic Model Training For Anomaly Detection on Sensor Data Streams in Meshed Edge Networks
摘要：
异常检测对于处理边缘和雾环境、智能城市以及工业4.0中的传感器数据量越来越重要。为了确保良好的结果，需要定期更新所使用的ML模型，以适应传感器数据中的季节变化和概念漂移。尽管边缘资源可用性的增加可以允许直接在设备上原位执行模型训练，但它仍然经常被卸载到雾设备或云。在本文中，我们提出了本地乐观调度（LOS），这是一种在数据源附近执行周期性ML模型训练作业的方法，而不会使轻量级边缘设备过载。训练作业根据需要卸载到附近的邻居节点，并且资源消耗被优化以满足训练周期，同时仍然确保足够的资源用于进一步的训练执行。这种调度是以分散、协作和机会主义的方式完成的，而不需要完全了解基础设施和工作负载。我们在现实世界数据集的边缘计算测试平台上评估了我们的方法。实验结果表明，LOS的地方接近输入传感器流的训练执行，减少训练时间和训练周期之间的偏差高达40%，并增加了成功调度的训练工作量相比，在原位执行。
英文摘要：
Anomaly detection is increasingly important to handle the amount of sensor data in Edge and Fog environments, Smart Cities, as well as in Industry 4.0. To ensure good results, the utilized ML models need to be updated periodically to adapt to seasonal changes and concept drifts in the sensor data. Although the increasing resource availability at the edge can allow for in-situ execution of model training directly on the devices, it is still often offloaded to fog devices or the cloud. In this paper, we propose Local-Optimistic Scheduling (LOS), a method for executing periodic ML model training jobs in close proximity to the data sources, without overloading lightweight edge devices. Training jobs are offloaded to nearby neighbor nodes as necessary and the resource consumption is optimized to meet the training period while still ensuring enough resources for further training executions. This scheduling is accomplished in a decentralized, collaborative and opportunistic manner, without full knowledge of the infrastructure and workload. We evaluated our method in an edge computing testbed on real-world datasets. The experimental results show that LOS places the training executions close to the input sensor streams, decreases the deviation between training time and training period by up to 40% and increases the amount of successfully scheduled training jobs compared to an in-situ execution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
