IECL: An Intelligent Energy Consumption Model for Cloud Manufacturing

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 12; pp. 8967 - 8976
• Main Authors: Zhou, Zhou, Shojafar, Mohammad, Alazab, Mamoun, Li, Fangmin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Cloud manufacturing; Confidence intervals; data center; Data centers; Data models; Energy consumption; energy consumption prediction; Feature extraction; Manufacturing; Power consumption; power model; Predictive models; Real time; Resource utilization; Search algorithms; Search methods; Servers; support vector machine (SVM); Support vector machines
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3165085

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 by a data center can help resource providers predict the total power consumption and improve resource utilization. To enhance the accuracy of server power models, we propose a real-time energy consumption prediction method called IECL that combines the support vector machine, random forest, and grid search algorithms. The random forest algorithm is used to screen the input parameters of the model, while the grid search method is used to optimize the hyperparameters. The error confidence interval is also leveraged to describe the uncertainty in the energy consumption by the server. Our experimental results suggest that the average absolute error for different workloads is less than 1.4% with benchmark models.