IECL: An Intelligent Energy Consumption Model for Cloud Manufacturing

The high computational capability provided by a data center makes it possible to solve complex manufacturing issues and carry out large-scale collaborative cloud manufacturing. Accurately, real-time estimation of the power required can help resource providers predict total consumption improve utilization. To enhance accuracy server models, we propose energy prediction method called IECL that combines support vector machine, random forest, grid search algorithms. forest algorithm is used screen input parameters model, while optimize hyperparameters. error confidence interval also leveraged describe uncertainty in server. Our experimental results suggest average absolute for different workloads less than 1.4% with benchmark models.