Monitoring Runtime Metrics of Fog Manufacturing via a Qualitative and Quantitative (QQ) Control Chart

Abstract

Fog manufacturing combines Fog and Cloud computing in a manufacturing network to provide efficient data analytics and support real-time decision-making. Detecting anomalies, including imbalanced computational workloads and cyber-attacks, is critical to ensure reliable and responsive computation services. However, such anomalies often concur with dynamic offloading events where computation tasks are migrated from well-occupied Fog nodes to less-occupied ones to reduce the overall computation time latency and improve the throughput. Such concurrences jointly affect the system behaviors, which makes anomaly detection inaccurate. We propose a qualitative and quantitative (QQ) control chart to monitor system anomalies through identifying the changes of monitored runtime metric relationship (quantitative variables) under the presence of dynamic offloading (qualitative variable) using a risk-adjusted monitoring framework. Both the simulation and Fog manufacturing case studies show the advantage of the proposed method compared with the existing literature under the dynamic offloading influence.