Deep Learning-based QoS Prediction for Manufacturing Cloud Service

Huifang Li, Wanwen Wei, Rui Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Multiple manufacturing cloud services (MCSs) are integrated in cloud manufacturing platform for providing service to internet users and its QoS has become an important evaluation indicator. Availability and reliability are two important properties of QoS. But few researches have been done on availability prediction and MCSs are always supposed to be available, while reliability is usually estimated by the empirical value or the mean value of historical executions. However, they both considered a little or even ignored the dynamic characteristics of cloud environment. This paper designed a deep learning based approach to predict QoS, i.e. availability and reliability, where availability prediction utilizes LSTM, and reliability prediction uses DNN model. To validate the effectiveness of the proposed method, the experiment is conducted and its results demonstrate that our approach outperforms the existing ones.

Original languageEnglish
Title of host publicationProceedings of the 38th Chinese Control Conference, CCC 2019
EditorsMinyue Fu, Jian Sun
PublisherIEEE Computer Society
Pages2719-2724
Number of pages6
ISBN (Electronic)9789881563972
DOIs
Publication statusPublished - Jul 2019
Event38th Chinese Control Conference, CCC 2019 - Guangzhou, China
Duration: 27 Jul 201930 Jul 2019

Publication series

NameChinese Control Conference, CCC
Volume2019-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference38th Chinese Control Conference, CCC 2019
Country/TerritoryChina
CityGuangzhou
Period27/07/1930/07/19

Keywords

  • Availability
  • DNN
  • Long Short-Term Memory
  • Manufacturing cloud service
  • Quality of Service (QoS)
  • Reliability
  • Time series

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