Cloud-load forecasting via decomposition-aided attention recurrent neural network tuned by modified particle swarm optimization

Recent improvements in networking technologies have led to a significant shift towards distributed cloud-based services. However, adequate management of computation resources by providers is vital to maintain the costs of operations and quality of services. A robust system is needed to forecast demand and prevent excessive resource allocations. Extensive literature review suggests that the potential of recurrent neural networks with attention mechanisms is not sufficiently explored and applied to cloud computing. To address this gap, this work proposes a methodology for forecasting load of cloud resources based on recurrent neural networks with and without attention layers. Utilized deep learning models are further optimized through hyperparameter tuning using a modified particle swarm optimization metaheuristic, which is also introduced in this work. To help models deal with complex non-stationary data sequences, the variational mode decomposition for decomposing complex series has also been utilized. The performance of this approach is compared to several state-of-the-art algorithms on a real-world cloud-load dataset. Captured performance metrics ( $$R^2$$ R 2 , mean square error, root mean square error, and index of agreement) strongly indicate that the proposed method has great potential for accurately forecasting cloud load. Further, models optimized by the introduced metaheuristic outperformed competing approaches, which was confirmed by conducted statistical validation. In addition, the best-performing forecasting model has been subjected to SHapley Additive exPlanations analysis to determine the impact each feature has on model forecasts, which could potentially be a very useful tool for cloud providers when making decisions.