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A Multivariate Cloud Workload Prediction Method Integrating Convolutional Nonlinear Spiking Neural Model with
Minglong He1, Nan Zhou1, Hong Peng1
1School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.
International Journal of Neural Systems
|September 30, 2025
Summary
This study introduces a novel hybrid model for multivariate workload prediction in cloud computing. The proposed model significantly improves forecasting accuracy, outperforming existing deep learning methods.
Area of Science:
- Cloud Computing
- Artificial Intelligence
- Time Series Analysis
Background:
- Multivariate workload prediction is crucial for efficient cloud resource management.
- Existing models struggle to capture complex inter-variable correlations and temporal dynamics.
Purpose of the Study:
- To develop an advanced model for accurate multivariate workload prediction.
- To enhance the capture of nonlinear data patterns and long-term temporal dependencies.
Main Methods:
- A hybrid model integrating a Nonlinear Spiking Neural P System (ConvNSNP) with a Bidirectional Long Short-Term Memory (BiLSTM) network was proposed.
- ConvNSNP extracts temporal and cross-variable dependencies, while BiLSTM strengthens long-term modeling.
- The model was evaluated on public cloud workload traces from Alibaba and Google.
Main Results:
- The proposed model demonstrated superior performance compared to various deep learning approaches (CNN, RNN, LSTM, TCN, LSTNet, CNN-GRU, CNN-LSTM).
- Achieved up to 9.9% improvement in Root Mean Square Error (RMSE) and 11.6% improvement in Mean Absolute Error (MAE).
- Showcased favorable performance in Mean Absolute Percentage Error (MAPE).
Conclusions:
- The hybrid ConvNSNP-BiLSTM model is highly effective for multivariate workload prediction in cloud environments.
- The model's ability to process nonlinear data and capture complex dependencies leads to improved forecasting accuracy.
- This research offers a significant advancement in cloud workload prediction methodologies.
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