Artificial intelligence enabled behavior modeling and dual-task performance analysis of cloud-native software with fused multi-source heterogeneous data

Abstract

Abstract To address the challenges of heterogeneous multi-source data, inadequate collaborative modeling of temporal and topological features, and low efficiency in dual-task optimization in performance prediction and bottleneck localization for cloud-native microservice systems, this paper proposes a Multi-Modal and Multi-Scale Temporal Propagation model (M $$^3$$ TP). The model achieves unified representation of logs, time-series metrics, and call-chain data through a Multi-modal Heterogeneous Embedding Module, simultaneously captures dynamic evolutionary patterns and service topological dependencies via a Temporal-Graph Joint Learning Module, and implements joint optimization of performance prediction and bottleneck localization using a Dual-Task Collaborative Decoding mechanism. Experiments on two public datasets, GAIA and PetShop, demonstrate that the M $$^3$$ TP model achieves $$R^2$$ scores of 0.95 and 0.96 for performance prediction, F1 scores of 0.93 and 0.94 for bottleneck localization, and inference latencies as low as 7.1ms and 6.5ms, respectively, outperforming 8 baseline models including LSTM, Informer, and GAT. Ablation studies validate the effectiveness of each core component, and case studies confirm the model’s capability in capturing performance fluctuations and identifying root-cause services accurately. The proposed model can effectively support cloud-native AIOps and provide a solid technical foundation for proactive monitoring and fault diagnosis of microservice systems.