一句话：提出 MSTGAD，用注意力多模态学习融合指标、日志与追踪并构建微服务系统孪生图，实现半监督实时异常检测。
微服务系统可靠性保障困难，现有异常检测多依赖单一数据模态（指标、日志或追踪），难以刻画模态间复杂关联与交互，易产生漏报和误报。本文提出半监督图异常检测方法 MSTGAD，将多模态数据通过注意力多模态学习融合。方法先从三类模态提取并归一化特征，再构建 MST（微服务系统孪生）图，节点表示服务实例，边表示调度关系；随后用带空间与时间注意力机制的 Transformer 网络建模模态间关联与数据点间时序依赖，以实时自动检测异常。在两个真实数据集上实验，MSTGAD 取得 0.961 F1 值，相较最优方法平均提升 4.85%。
Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services. Despite its benefits, ensuring the reliability and safety of a microservice system remains highly challenging. Existing anomaly detection algorithms based on a single data modality (i.e., metrics, logs, or traces) fail to fully account for the complex correlations and interactions between different modalities, leading to false negatives and false alarms, whereas incorporating more data modalities can offer opportunities for further performance gain. As a fresh attempt, we propose in this paper a semi-supervised graph-based anomaly detection method, MSTGAD, which seamlessly integrates all available data modalities via attentive multi-modal learning. First, we extract and normalize features from the three modalities, and further integrate them using a graph, namely MST (microservice system twin) graph, where each node represents a service instance and the edge indicates the scheduling relationship between different service instances. The MST graph provides a virtual representation of the status and scheduling relationships among service instances of a real-world microservice system. Second, we construct a transformer-based neural network with both spatial and temporal attention mechanisms to model the inter-correlations between different modalities and temporal dependencies between the data points. This enables us to detect anomalies automatically and accurately in real-time. Extensive experiments on two real-world datasets verify the effectiveness of our proposed MSTGAD method, achieving competitive performance against state-of-the-art approaches, with a 0.961 F1-score and an average increase of 4.85%. The source code of MST-GAD is publicly available at https://github.com/ant-research/microservice_system_twin_graph_based_anomaly_detection.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{HuangYYLZ23,
title = {Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System},
author = {Jun Huang and Yang Yang and Hang Yu and Jianguo Li and Xiao Zheng},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00138}
}