US US20260230378A1
[0000] A system and method for configuring cloud service monitors using an attention-enhanced graph neural network (GNN) are presented. The system constructs an input graph representing relationships between entities such as monitors, metrics, and dimensions. The GNN employs multi-head attention mechanisms to focus on relevant node relationships. A scoring mechanism evaluates enriched node representations, generating predicted probability scores for potential configuration relationships. Link prediction is used to determine valid configurations, resulting in tailored recommendations for cloud service monitors. The model incorporates a composite loss function, including diversity and ranking losses. By leveraging service dependency and reliability data, the system provides context-aware configuration recommendations, enabling the monitoring capabilities of cloud services.
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