GAL-MAD：使用图注意力网络实现微服务应用中的可解释异常检测
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
AI总结：
本文提出GAL-MAD模型，结合图注意力网络和LSTM捕获微服务时空依赖，利用SHAP值定位异常根因，在RS-Anomic数据集上实现了高准确率与可解释的异常检测。
摘要：
向微服务的转变彻底改变了软件架构，提供了更好的可扩展性和模块化。然而，微服务的分布式和动态特性在确保系统可靠性方面引入了复杂性，使得异常检测对于维持性能和功能至关重要。源于网络和性能问题的异常必须被迅速识别和解决。现有的异常检测技术通常依赖于统计模型或机器学习方法，这些方法难以处理微服务应用中固有的高维、相互依赖的数据。当前技术和可用数据集主要关注系统追踪和日志，限制了它们支持高级检测模型的能力。本文通过引入使用开源RobotShop微服务应用生成的RS-Anomic数据集来填补这些空白。该数据集捕获了正常和异常条件下的多变量性能指标和响应时间，包含十种类型的异常。我们提出了一种名为Graph Attention and LSTM-based Microservice Anomaly Detection（GAL-MAD）的新型异常检测模型，利用Graph Attention和Long Short-Term Memory架构来捕获微服务中的空间和时间依赖性。我们利用SHAP值来定位异常服务并识别根本原因，以增强可解释性。实验结果表明，GAL-MAD在RS-Anomic数据集上优于最先进的模型，在不同的异常率下实现了更高的准确率和召回率。这些解释为服务异常提供了可操作的见解，使系统管理员受益。
英文摘要：
The transition to microservices has revolutionized software architectures, offering enhanced scalability and modularity. However, the distributed and dynamic nature of microservices introduces complexities in ensuring system reliability, making anomaly detection crucial for maintaining performance and functionality. Anomalies stemming from network and performance issues must be swiftly identified and addressed. Existing anomaly detection techniques often rely on statistical models or machine learning methods that struggle with the high-dimensional, interdependent data inherent in microservice applications. Current techniques and available datasets predominantly focus on system traces and logs, limiting their ability to support advanced detection models. This paper addresses these gaps by introducing the RS-Anomic dataset generated using the open-source RobotShop microservice application. The dataset captures multivariate performance metrics and response times under normal and anomalous conditions, encompassing ten types of anomalies. We propose a novel anomaly detection model called Graph Attention and LSTM-based Microservice Anomaly Detection (GAL-MAD), leveraging Graph Attention and Long Short-Term Memory architectures to capture spatial and temporal dependencies in microservices. We utilize SHAP values to localize anomalous services and identify root causes to enhance explainability. Experimental results demonstrate that GAL-MAD outperforms state-of-the-art models on the RS-Anomic dataset, achieving higher accuracy and recall across varying anomaly rates. The explanations provide actionable insights into service anomalies, which benefits system administrators.
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。
