论文标题
传感器流上黑盒机器学习服务的有效运行时分析
Efficient Runtime Profiling for Black-box Machine Learning Services on Sensor Streams
论文作者
论文摘要
在高度分布的环境（例如云，边缘和雾计算）中，机器学习在自动化和优化过程中的应用正在上升。机器学习工作经常应用于流媒体条件下，其中模型用于分析源自例如视频流或感官数据。通常，在下一个数据到达之前，需要及时提供特定数据样本的结果。因此，必须提供足够的资源以确保特定数据流的及时处理。本文着重于为容器化的机器学习工作提出运行时建模策略，从而可以对每个作业和组件的资源进行优化和自适应调整。我们的Black-Box方法将多种技术组装成有效的运行时分析方法，同时却没有对基本硬件，数据流或应用机器学习作业的假设。结果表明，我们的方法能够在短期分析阶段捕获不同机器学习作业的一般运行时行为。
In highly distributed environments such as cloud, edge and fog computing, the application of machine learning for automating and optimizing processes is on the rise. Machine learning jobs are frequently applied in streaming conditions, where models are used to analyze data streams originating from e.g. video streams or sensory data. Often the results for particular data samples need to be provided in time before the arrival of next data. Thus, enough resources must be provided to ensure the just-in-time processing for the specific data stream. This paper focuses on proposing a runtime modeling strategy for containerized machine learning jobs, which enables the optimization and adaptive adjustment of resources per job and component. Our black-box approach assembles multiple techniques into an efficient runtime profiling method, while making no assumptions about underlying hardware, data streams, or applied machine learning jobs. The results show that our method is able to capture the general runtime behaviour of different machine learning jobs already after a short profiling phase.
