Optimizing energy efficiency in cloud environments through hybrid workload prediction using CNN and LSTM

Abstract

Energy efficiency is becoming a crucial factor in addressing rising operational expenses, environmental sustainability, and energy supply constraints due to the increasing demand for computational resources and the rapid expansion of edge and cloud computing. Therefore, accurate workload prediction has been crucial for achieving energy efficiency in Edge and Cloud computing. This research evaluates the performance of three deep learning models namely, CNN-only, LSTM-only, and a hybrid CNN-LSTM for forecasting workload patterns using historical data. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² were used to assess model performance. The CNN-only model showed limited accuracy, reflecting its weakness in capturing temporal dependencies. In contrast, the LSTM-only model demonstrated near-perfect prediction accuracy due to its ability to model sequential data effectively. The CNN-LSTM hybrid model slightly outperformed the LSTM model, combining spatial feature extraction with temporal learning for enhanced results. These findings highlight the superiority of temporal or hybrid architectures in time-series workload prediction. Future work will focus on integrating attention mechanisms, deploying models in edge computing environments, and incorporating contextual features to improve model adaptability and realworld applicability in hybrid cloud-edge systems.