Modeling Contextual Dependencies and Temporal Dynamics in Cloud Backend Environments

Abstract

This paper proposes a Context-Aware Temporal Dynamic Modeling (CATDM) method to address the challenges of dynamic dependency and contextual coupling among multidimensional metric sequences in cloud backend environments, aiming to achieve unified representation of temporal evolution features and semantic contextual information in complex systems. The method first constructs a multi-scale temporal feature extraction module that captures short-term fluctuations and long-term trends in system states through convolutional scale decomposition and dynamic weight fusion. Then, a conditional dependency matrix is introduced to characterize feature correlations and dependency strengths under different contextual scenarios, forming a dynamically adaptive structural representation that evolves with environmental changes. Based on this, a joint transformation layer is designed to fuse temporal and contextual features, generating implicit state vectors with global consistency and semantic stability. Finally, a temporal consistency constraint is applied to ensure feature smoothness and dependency continuity across time steps, enhancing model robustness and generalization under non-stationary distributions. The proposed approach demonstrates superior performance in cloud backend load forecasting and metric modeling tasks, effectively capturing dynamic dependencies and contextual couplings in multi-tenant environments while significantly reducing modeling bias caused by cross-domain transfer and service heterogeneity. Experimental results confirm that the method achieves notable improvements in multi-scale modeling capability, contextual adaptability, and temporal consistency compared with traditional models, providing an efficient and scalable solution for intelligent monitoring and dynamic optimization in cloud backend systems.