Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses. To ensure reliable performance, it's essential to accurately identify and localize these issues using service monitoring metrics. Given the complexity and scale of modern cloud systems, this task can be challenging and may require extensive expertise and resources beyond the capacity of individual humans. Some existing methods tackle this problem by analyzing each metric independently to detect anomalies. However, this could incur overwhelming alert storms that are difficult for engineers to diagnose manually. To pursue better performance, not only the temporal patterns of metrics but also the correlation between metrics (i.e., relational patterns) should be considered, which can be formulated as a multivariate metrics anomaly detection problem. However, most of the studies fall short of extracting these two types of features explicitly. Moreover, there exist some unlabeled anomalies mixed in the training data, which may hinder the detection performance. To address these limitations, we propose the Relational- Temporal Anomaly Detection Model (RTAnomaly) that combines the relational and temporal information of metrics. RTAnomaly employs a graph attention layer to learn the dependencies among metrics, which will further help pinpoint the anomalous metrics that may cause the anomaly effectively. In addition, we exploit the concept of positive unlabeled learning to address the issue of potential anomalies in the training data. To evaluate our method, we conduct experiments on a public dataset and two industrial datasets. RTAnomaly outperforms all the baseline models by achieving an average F1 score of 0.929 and Hit@3 of 0.920, demonstrating its superiority.

翻译：性能问题普遍存在于大规模云服务系统中，可能导致巨大的收入损失。为确保可靠性能，必须利用服务监控指标精确识别并定位这些问题。鉴于现代云系统的复杂性和规模，这一任务极具挑战性，可能需要远超个人能力的专业知识和资源。现有部分方法通过独立分析每个指标来检测异常，但这可能引发工程师难以手动诊断的告警风暴。为实现更优性能，不仅需考虑指标的时序模式，还需考虑指标间的相关性（即关系模式），这可表述为多变量指标异常检测问题。然而，现有研究大多未能显式提取这两类特征。此外，训练数据中混有未标注异常，可能影响检测性能。针对这些局限，我们提出结合指标关系与时序信息的关系-时序异常检测模型（RTAnomaly）。该模型采用图注意力层学习指标间的依赖关系，进而有效定位可能引发异常的异常指标。同时，我们利用正无标记学习概念处理训练数据中的潜在异常。为评估方法性能，我们在一个公开数据集和两个工业数据集上进行实验。RTAnomaly以平均F1得分0.929和Hit@3得分0.920超越所有基线模型，彰显其优越性。