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Causality-Aware Dual Graph Approach for Multi-Cloud Networking Forecasting
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AI
DOI：10.1109/tnse.2026.3691846.png)
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Abstract
En 中文
Multi-cloud networking enables enterprises to orchestrate applications across heterogeneous cloud platforms, offering improved scalability, resilience, and cost-effectiveness. However, complex interactions among distributed cloud resources introduce intricate interdependencies, particularly through inter-VM traffic flows and shared network pathways. Current forecasting methods for system metrics often overlook these fine-grained traffic dynamics and the causal relationships inherent in multi-cloud environments, failing to capture how resource usage in one VM can influence others. In this paper, we propose a causality-aware dual-graph neural network designed explicitly for multivariate system metric forecasting in multi-cloud settings. Our framework employs an adaptive dual-graph architecture to simultaneously model spatial dependencies among metrics across different VMs and temporal dependencies within individual VM metrics. To explicitly encode directional causal relationships, we construct a Transfer Entropy-based causality matrix optimized through Bayesian search. A causally guided fusion module integrates these representations using contrastive and triplet learning, aligning latent spaces to emphasize causality-aware structures. Extensive experiments on real-world multi-cloud traces demonstrate that our approach consistently outperforms strong spatio-temporal and Transformer-based forecasting baselines, achieving relative error reductions of at least 8% (across MAE/RMSE/MAPE) in the 15-VM setting.
Keywords:
Cloud networking
graph neural network
time series
granger causality
alignment
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7.9
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2.5K
Citations:
10.0K