# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 2 Related Work  
In this section, we review and summarize relevant research in the field of workload forecasting, especially the application of statistical methods, machine learning and deep learning techniques, ensemble learning methods, and time-dynamic modeling techniques based on matrix factorization. These methods provide solution ideas for workload forecasting from different perspectives and also have certain limitations. Statistical methods are used as an early research focus for workload forecasting, capturing short-term trends in workload data through time series analysis. For example, Ref. [9] predicts future performance trends based on statistical models and triggers system expansion using response time as an indicator; Ref. [25] applies an ARIMA model to enable medium-term workload forecasting for power systems; Ref. [10] uses an ARIMA model to optimize CPU utilization prediction and resource allocation to reduce energy consumption. These methods utilize time series autocorrelation and perform well in scenarios where the workload data are stable and linear in nature. However, they typically assume data linearity and hence are unable to handle nonlinear data arising in diverse scenarios. Machine learning (ML) methods are capable of modeling complex temporal dynamics and nonlinear relationships and are important tools for workload forecasting. For example, Ref. [12] uses decision tree regression to dynamically adapt a resource forecasting model for short-term workload measurements, and Ref. [13] transforms workload request forecasts into resource utilization forecasts to optimize system stability. These methods enhance the ability to adapt to nonlinear variations by learning complex patterns in historical data. These models focus only on the temporal dynamic properties of a single microservice and fail to capture the inter-service dependencies that are critical in workload propagation. Further research attempts to combine temporal dynamics with dependency modeling. For example, Ref. [20] improves the prediction ability of the microservice workflow by integrating the dependency structure into deep learning models through the directed acyclic graph (DAG). Ref. [26] proposes a workload prediction method based on spatio-temporal graph neural networks, which combines temporal dynamics with inter-microservice dependencies to significantly improve prediction performance. However, these methods rely on complex graph neural network architectures and capture only static dependencies. ASTMGCNet [27] is designed to improve prediction accuracy in dynamic urban traffic systems by effectively capturing complex spatio-temporal correlations through multi-scale feature extraction and dual attention mechanisms to effectively capture complex spatio-temporal correlations within traffic sequences, enhancing prediction accuracy. Based on it, Ref. [28] employs a resource allocation strategy within edge computing to reduce energy usage during prediction, where the attention mechanism helps quickly decide which services are most important. Using this information, smart cities can assign tasks and allocate resources based on priority to ensure high-quality service. The work [29] discusses the limitations of current deep-learning architectures in using publicly available spatial data to capture crop-yield complexity, and integrates process-based methods to enhance the reliability and interpretability of data-driven forecasting models. Overall, ML methods, especially deep learning methods, are highly dependent on the quality of data. They have limited generalization ability in diverse scenarios using a single model since it would require frequent adjustments and retraining, making the maintenance cost excessively high. To cope with the limitations of a single model, ensemble learning has gradually become a hot research direction in the field of workload prediction. Ensemble learning techniques improve the accuracy and robustness of prediction by combining the advantages of multiple models. For example, [PERSON].