As a critical technology, anomaly detection ensures the smooth operation of cloud systems while maintaining the market competitiveness of cloud service providers. However, the resource data in real-world cloud systems is predominantly unannotated, leading to insufficient supervised signals for anomaly detection. Moreover, complicated topological associations existed between cloud servers (e.g., computation, storage, and communication). While acquiring resource information, correlating the system topology is challenging. To this end, we propose the GCAD for cloud system anomaly detection, which integrates data augmentation, GraphGRU, contrastive learning, and reconstruction. First, GCAD constructs positive and negative sample pairs through the masking and Gaussian noise data augmentation. Then, the GraphGRU processes extended temporal graph data, extracting and fusing spatiotemporal features from resource status and system topology. In addition, GCAD introduces linear attention for encoding spatiotemporal representations to capture their global correlation information. The weight parameters of the encoder are optimized using a contrastive learning mechanism. Finally, GCAD utilizes a reconstruction technique to calculate anomaly scores, facilitating the evaluation of the state of the cloud system at each time point. Experimental results indicate that GCAD outperforms state-of-the-art compared methods on two real-world datasets that contain topology information.

异常检测作为一项关键技术，在保证云系统平稳运行的同时，保持云服务提供商的市场竞争力。然而，现实世界云系统中的资源数据主要是未注释的，导致用于异常检测的监督信号不足。此外，云服务器之间存在复杂的拓扑关联（例如，计算、存储和通信）。在获取资源信息时，关联系统拓扑是具有挑战性的。为此，我们提出了用于云系统异常检测的 GCAD，它集成了数据增强、GraphGRU、对比学习和重建。首先，GCAD 通过掩蔽和高斯噪声数据增强构建正负样本对。然后，GraphGRU 处理扩展的时态图数据，从资源状态和系统拓扑中提取和融合时空特征。此外，GCAD 引入了线性注意力来编码时空表示以捕获其全局关联信息。编码器的权重参数使用对比学习机制进行优化。最后，GCAD 利用重建技术来计算异常分数，从而有助于评估每个时间点的云系统状态。实验结果表明，GCAD 在两个包含拓扑信息的真实数据集上优于最先进的比较方法。