Manod: A multi-modal anomaly detection framework for distributed system

Distributed infrastructure has been widely deployed in large-scale software systems in recent years to meet the growing demand for applications, due to its scalability and resource-sharing characteristics. Accurately predicting and identifying anomalies is critical to ensure the stable and reliable running of complex distributed systems. System abnormalities can often be reflected through key performance indicators and logs. Metrics provide quantitative measures of system performance and operational status, while logs record various events that occur in the system. Current approaches typically rely on a single data source to detect anomalies, which may lead to false positives and limit the accuracy of failure detection. A combination of these two data modalities can provide a comprehensive view of the system behavior. In this work, we propose a semi-supervised fault detection method, Manod, to monitor the health state of the system based on multimodal data. To obtain the discriminative representations, it employs a graph-based hierarchical encoding approach and leverages pre-trained language models for modeling metrics and logs, respectively. Then, it adopts a novel gated attention fusion method to integrate heterogeneous information. Extensive experiments on two datasets validate the effectiveness of our proposed Manod. It achieves F1-scores of 0.870 and 0.934 on one simulation dataset (D1) and one real-world dataset (D2), respectively, and significantly outperforms all baseline models. This demonstrates its capacity in mitigating both false positives and false negatives.