Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments

Anomaly detection in cloud environments remains both critical and challenging. Existing context-level benchmarks typically focus on either metrics or logs and often lack reliable annotation, while most detection methods emphasize point anomalies within a single modality, overlooking contextual signals and limiting real-world applicability. Constructing a benchmark for context anomalies that combines metrics and logs is inherently difficult: reproducing anomalous scenarios on real servers is often infeasible or potentially harmful, while generating synthetic data introduces the additional challenge of maintaining cross-modal consistency. Ensuring the stability and availability of large-scale cloud systems is of great importance (Kazemzadeh & Jacobsen, 2009; Bu et al., 2018; Zhang et al., 2015). Accurate detection methods that can also identify among anomaly scenarios are essential to mitigate potential losses (Zhang et al., 2018; Barbhuiya et al., 2018a). Large-scale cloud systems usually generate abundant logs and expose various metrics, both of which serve as some of the most valuable data sources for anomaly detection (Lin et al., 2016; Nandi et al., 2016). Numerous benchmarks have been proposed for cloud anomaly detection such as (Oliner & Stearley, 2007; Xu et al., 2009; Akmeemana et al., 2025). However, most existing research and benchmarks for cloud anomaly detection have focused on point anomalies, where deviations are identified in isolation within a single modality, such as metrics or logs. Although these benchmarks have provided the community with relevant evaluation testbeds, they capture only a narrow slice of the anomaly landscape and often fail to reflect the complexity of real cloud environments.