M3-DD: Detecting and Localizing Multiple Concurrent Performance Degradations in Cloud Systems

Performance degradation is one of the most pressing threats to the reliability of modern cloud systems, yet its root causes are latent, entangled, and rarely confined to a single metric. Many available diagnostic benchmarks reduce degradation to binary or single-fault labels rather than multiple time-bounded events that may overlap. In this paper, we present the Multi-modal Concurrent Degradation Dataset (MCDD), a VM-level benchmark assembled from production observations, with 22 metric channels, a vocabulary of 122 low-level anomaly-event types, and interval annotations for 25 degradation categories. These annotations preserve multiple overlapping events. We further propose M${}^3$-DD (Multi-Modal Multi-instance Degradation Detector), which uses object queries to jointly classify and localize overlapping degradation intervals. Its interval-aware attention fuses metric time series with low-level anomaly events, allowing the decoder to represent their temporal relationships before predicting event sets. We evaluate native concurrent event sets using Coverage–Localization Balance (CLB), a composite of event-level alert coverage and localization precision, alongside confidence-ranked average mAP. On MCDD, M${}^3$-DD attains the highest CLB (0.4180) and average mAP (0.1967) among the evaluated native-view methods. These results highlight M${}^3$-DD’s ability to detect and localize concurrent degradations.