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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 aqualitative 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.

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