Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling

TLDR

Cyber‑manufacturing systems interconnect facilities via sensing and actuation networks to provide reliable computation and communication services, yet most advanced data analytics are centralized or cloud‑based, limiting responsiveness for online decision‑making. The study proposes decomposing existing advanced data analytics models for CMS into distributed variants using ADMM. A simulation study evaluates the distributed method on a Fog‑Cloud network, comparing it to a pure Cloud system using six performance metrics from the literature. The distributed method improves computation services in a Fog‑Cloud network, and the evaluation shows how Fog‑Cloud architecture choices affect performance, guiding future efficient designs.

Abstract

Cyber-manufacturing systems (CMS) interconnect manufacturing facilities via sensing and actuation networks to provide reliable computation and communication services in smart manufacturing. In CMS, various advanced data analytics have been proposed to support effective decision-making. However, most of them were formulated in a centralized manner to be executed on single workstations, or on Cloud computation units as the data size dramatically increases. Therefore, the computation or communication service may not be responsive to support online decision-making in CMS. In this research, a method to decompose a group of existing advanced data analytics models (i.e., family learning for CMS modeling) into their distributed variants is proposed via alternative direction method of multipliers (ADMM). It improves the computation services in a Fog-Cloud computation network. A simulation study is conducted to validate the advantages of the proposed distributed method on Fog-Cloud computation network over Cloud computation system. Besides, six performance evaluation metrics are adopted from the literature to access the performance of computation and communication. The evaluation results also indicate the relationship between Fog-Cloud architectures and computation performances, which can contribute to the efficient design of Fog-Cloud architectures in the future.