关键词: 云工作流系统, 混沌时间序列, 相空间重构, 径向基函数神经网络, 时间预测

Abstract: 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.

Key words: cloud workflow system, chaotic time series, reconstructed phase space, radical basis function neural network, time prediction