Runtime Anomaly Detection for MEC Services with Multi-Timescale and Dimensional Feature

Mobile Edge Computing (MEC) has emerged as a distributed computing paradigm offering low-latency services to users. However, the distributed and intricate deployment inevitably makes it challenge to ensure the reliability of MEC services. Anomaly detection using the streaming data of MEC services is an essential way to address the challenge. In this paper, to improve the detection accuracy and efficiency, we propose an MTDF-Detection framework for MEC services, jointly extracting Multi-Timescale and Dimensional Feature (MTDF), including the long-term trend, periodic, short-term fluctuation and auxiliary parameters (e.g., system maintenance and offloading task). In this framework, to reduce the computing costs, we adopt a Bidirectional Simple Recurrent Unit (Bi-SRU) to obtain contextual feature; and we design an adaptive m-Sigma algorithm to determine the dynamic threshold with real-time streaming data. Extensive experiments are conducted on a real-world dataset, and the results demonstrate that the MTDF-Detection framework outperforms the state-of-the-art schemes in terms of accuracy and efficiency.