基于孪生图的微服务系统注意力多模态学习异常检测
Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System
AI总结：
针对微服务系统单模态异常检测易漏报错报的问题，提出基于微服务系统孪生图的半监督方法MSTGAD，融合多模态特征并结合时空注意力transformer实现精准实时异常检测，代码已开源。
摘要：
近年来，微服务架构因具备独立部署和扩展服务的能力，在企业应用管理领域快速兴起。尽管优势显著，但保障微服务系统的可靠性与安全性仍极具挑战。现有基于单一数据模态（即指标、日志或调用链）的异常检测算法无法充分捕捉不同模态间的复杂关联与交互，会导致漏报与误报，而融合更多数据模态可为性能提升创造空间。作为一项全新尝试，本文提出一种半监督的基于图的异常检测方法MSTGAD，该方法通过注意力多模态学习无缝整合所有可用数据模态。首先，我们从三种模态中提取并归一化特征，再利用名为MST（微服务系统孪生）图的图结构对其进行进一步融合：图中每个节点代表一个服务实例，边表示不同服务实例间的调度关系，MST图为真实微服务系统中服务实例的状态与调度关系提供了虚拟表征。其次，我们构建了一个同时具备空间与时间注意力机制的transformer神经网络，用于建模不同模态间的内部关联以及数据点间的时间依赖关系，从而实现自动、准确的实时异常检测。MSTGAD的源代码已公开于https://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection。
英文摘要：
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. The source code of MSTGAD is publicly available at https://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
