Online Computation Performance Analysis for Distributed Machine Learning Pipelines in Fog Manufacturing

Abstract

Smart manufacturing enables real-time data streaming from interconnected manufacturing processes to improve manufacturing quality, throughput, flexibility, and cost reduction via computation services. In these computation services, machine learning pipelines integrate various types of computation method options to match the contextualized, on-demand computation needs for the maximum prediction accuracy or the best model structure interpretation. On the other hand, there is a pressing need to integrate Fog computing in manufacturing, which will reduce communication time latency and dependency on connections, improve responsiveness and reliability of the computation services, and maintain data privacy. However, there is a knowledge gap in using machine learning pipelines in Fog manufacturing. Existing offloading strategies are not effective, due to the lack of accurate prediction model for the performance of computation services before the execution of those heterogeneous computation tasks. In this paper, machine learning pipelines are implemented in Fog manufacturing. The computation performance of each sub-step of pipelines is predicted and analyzed via linear regression models and random forest regression models. A Fog manufacturing testbed is adopted to validate the performance of the employed models. The results show that the models can adequately predict the performance of computation services, which can be further integrated into Fog manufacturing to better support offloading strategies for machine learning pipelines.