LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning

Microservice architecture is a high-cohesion and low-coupling software architecture. Its core idea is to split the application into a set of microservices with a single function and independent deployment. Due to their complexity and large scale, microservice systems are typically fragile and failures are inevitable. Therefore, there is an urgent need for fast and accurate anomaly detection capabilities. However, the existing microservice anomaly detection methods do not pay attention to the multi-source data of the microservice system and thus have low accuracy. To address this limitation, we propose a Log-Metric Combined Microservice Anomaly Detection approach through Graph-based Deep Learning (termed as LMGD). First, we propose a time-aware LSTM prediction neural network to improve the accuracy of service dependency mining. Secondly, based on the service dependency graph, we propose an anomaly detection method based on log-metric fusion, which can more accurately describe the running status of microservices, thereby improving the accuracy of anomaly detection. The experimental outcomes demonstrate that compared with other state-of-the-art methods, our method improves recall and F1-score by 2.63% and 1.05%.