学习分布式云应用程序中的异常以识别和定位异常
Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
摘要：
大型分布式云应用程序的操作和维护可能会迅速变得难以管理的复杂性，当出现问题时，会使操作人员承受巨大的压力。利用机器学习来识别和定位此类系统中的异常支持人类专家并实现快速缓解。然而，由于系统组件的各种相互依赖性，异常不仅影响它们的起源，而且通过分布式系统传播。考虑到这一点，我们提出了Arvalus及其变体D-Arvalus，这是一种神经图转换方法，将系统组件建模为节点及其依赖关系和位置作为边缘，以提高异常的识别和定位。给定一系列度量KPI，我们的方法预测最可能的系统状态-正常或异常类-并在检测到异常时执行本地化。在我们的实验中，我们模拟了分布式云应用程序部署和综合注入异常。评估结果表明，一般良好的预测性能的Arvalus，并揭示了D-Arvalus的优势，它结合了系统组件的依赖关系的信息。
英文摘要：
Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur. Utilizing machine learning for identification and localization of anomalies in such systems supports human experts and enables fast mitigation. However, due to the various inter-dependencies of system components, anomalies do not only affect their origin but propagate through the distributed system. Taking this into account, we present Arvalus and its variant D-Arvalus, a neural graph transformation method that models system components as nodes and their dependencies and placement as edges to improve the identification and localization of anomalies. Given a series of metric KPIs, our method predicts the most likely system state - either normal or an anomaly class - and performs localization when an anomaly is detected. During our experiments, we simulate a distributed cloud application deployment and synthetically inject anomalies. The evaluation shows the generally good prediction performance of Arvalus and reveals the advantage of D-Arvalus which incorporates information about system component dependencies.
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。
