@inproceedings{cc9674ef98b84dc8b70709efa94ef0c4,
title = "Predicting execution time of manufacturing cloud services using BP neural network",
abstract = "With the rapid development of Cloud Manufacturing technology, the number of services with the same or similar functions have emerged greatly on the platform. The existing research of predicting execution time of manufacturing cloud services is relatively few and the service execution time is mostly estimated by the average of historical executions. However, execution time changes dynamically in the cloud manufacturing environment. This paper divides execution time into static time and dynamic time, and then proposes its corresponding manufacturing cloud service execution time prediction approach. Static time can be calculated by formula, and on the basis of analyzing the influencing factors of the execution time, a BP neural network is used to predict the dynamic time from historical data. Experimental results demonstrate that the proposed approach can outperform the existing methods in improving the prediction accuracy of execution time.",
keywords = "BP neural network, cloud manufacturing, execution time prediction, influencing factor",
author = "Huifang Li and Haonan Lv and Baihai Zhang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Conference on Big Data Analysis, ICBDA 2017 ; Conference date: 10-03-2017 Through 12-03-2017",
year = "2017",
month = oct,
day = "20",
doi = "10.1109/ICBDA.2017.8078766",
language = "English",
series = "2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "887--892",
booktitle = "2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017",
address = "United States",
}