萝莉社

Summary: Edge-cloud systems consist of heterogeneous computational infrastructures designed to efficiently manage distributed workloads. Accurate performance prediction is essential for optimizing resource allocation, reducing latency, and improving system efficiency. This study applies machine learning models鈥擱andom Forest (RF), Long Short-Term Memory (LSTM), Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), and a hybrid RNN-DNN model鈥攖o predict key performance metrics. Using performance datasets, these models are evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results show that RF achieves the highest predictive accuracy, while deep learning models like LSTM and DNN yield varying effectiveness. The hybrid RNN-DNN model balances complexity and accuracy. These findings underscore the potential of ML-based approaches for optimizing resource management and performance in cloud-edge architectures.
Questions: (i) What additional performance metrics could provide deeper insights into model effectiveness? (ii) How can hyperparameter tuning further enhance the accuracy of deep learning models in this context? (iii) Would alternative ML approaches, such as ensemble learning or transformer-based models, improve predictions? (iv)What are the real-world deployment challenges of these models in edge-cloud environments?