Deakin University
Browse

File(s) under permanent embargo

Forecasting model for activity durations in cloud workflow based on chaotic time series

journal contribution
posted on 2013-08-01, 00:00 authored by Z J Wu, Xiao LiuXiao Liu, Z W Ni
Aiming at the problem that the linear time series did not efficiently predict the activity durations of cloud workflow, a forecasting model for activity durations in cloud workflow systems based on chaotic time series was proposed. The reconstructed phase space theory and Radical Basis Function (RBF) neural network was employed by this model to predict nonlinear time series. The reconstructed phase space theory could depict the nonlinear characteristics of cloud workflow due to system performance, network conditions and other factors, and RBF neural network was proved to be suitable for predicting chaotic time series. Computation intensive scientific applications and instance intensive business applications were taken into account in simulation scenarios, and the results showed that the proposed chaotic time series model was superior to the existing representative time-series forecasting strategies for both long-duration and short-duration activities.

History

Journal

Computer Integrated Manufacturing Systems, CIMS [Jisuanji Jicheng Zhizao Xitong ]

Volume

19

Issue

8

Pagination

1920 - 1927

Publisher

Beijing Advanced Manufacturing Technology Consultation Center

Location

Beijing, China

ISSN

1006-5911

Language

Chi

Publication classification

C Journal article; C1.1 Refereed article in a scholarly journal

Copyright notice

2017, China Academic Journal Electronic Publishing House

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC