Concept Drift-Based Runtime Reliability Anomaly Detection for Edge Services Adaptation

To meet the rapidly increasing need of computation-intensive and latency-sensitive applications, mobile edge computing (MEC) has attracted tremendous attention from both academia and industry. However, the runtime reliability of edge services fluctuates over time due to the dynamics in their internal states and the external environment. This causes the distribution of edge services' reliability data streams to vary in the form of concept drift. Severe negative reliability drifts indicate that an edge service may be suffering from a performance anomaly or a runtime failure. To ensure the stable operation of edge services, we propose A-Detection, a concept drift-based runtime reliability anomaly detection approach for edge services adaptation. We integrate reservoir sampling and singular value decomposition (SVD) for large-scale streaming data sampling and feature extraction. Jensen Shannon (JS) divergence is utilized to develop a dissimilarity metric of data stream distribution, called FDC, for runtime edge service reliability anomaly detection. When an anomaly is detected in a running edge service, checkpoint-retry is combined with computation offloading to implement runtime reliability adaptation. Extensive experimental results verify and demonstrate the effectiveness and efficiency of A-Detection.