IECL: An Intelligent Energy Consumption Model for Cloud Manufacturing

Zhou Zhou, Mohammad Shojafar, Mamoun Alazab, Fangmin Li

Research output: Contribution to journalArticlepeer-review

Abstract

The high computational capability provided by a data centre makes it possible to solve complex manufacturing issues and carry out large-scale collaborative cloud manufacturing. Accurate, real-time estimation of the power required by a data centre can help resource providers predict the total power consumption and improve resource utilisation. 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 optimise 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.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusE-pub ahead of print - 5 Apr 2022

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