Memory-augment graph transformer based unsupervised detection model for identifying performance anomalies in highly-dynamic cloud environments
Abstract Cloud computing systems provide highly available and scalable computing, storage, and network resources to meet various service demands. Anomaly detection based on monitoring metrics plays a crucial role in identifying system defects and abnormal behaviors, ensuring the reliability and stab...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-025-00766-5 |
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