Trace-based microservice anomaly detection through deep learning

Laigang Bai; Cheng Zhang

doi:10.1117/12.2674784

2 May 2023 Trace-based microservice anomaly detection through deep learning

Laigang Bai, Cheng Zhang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422T (2023) https://doi.org/10.1117/12.2674784
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Over the years, microservices have become increasingly popular and are being adopted more and more, microservice architecture is gradually replacing monolithic applications as the mainstream architecture. Anomaly detection for microservices is a hot topic of current research, which is important to ensure the security and performance of the application. This paper proposes a deep learning based anomaly detection method for microservices, which first serializes trace data, then trains a model combining AE and LSTM to detect anomalies, and also detects performance anomalies by executing time series. We evaluated our approach on a widely adopted microservice system and showed that our approach outperformed current trace-based approaches.

Citation Download Citation

Laigang Bai and Cheng Zhang "Trace-based microservice anomaly detection through deep learning", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422T (2 May 2023); https://doi.org/10.1117/12.2674784

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