Task scheduling model based on resource prediction for grid computing

Abstract

Resources in grid computation environments are heterogeneous and dynamic, and tasks in grid computation environments are executed by computers from different domains or clusters of virtual organization synergistically; the static task scheduling is not fit for tasks execution in grid computing environments. In the paper, a task scheduling model based on the results of resources prediction was proposed. Firstly, a method of weighted least square estimation was given to construct Autoregressive Moving Average ( ARMA) model, which would be applied in CPU load prediction of grid computer. After modeling a kind of data parallel grid tasks, the task scheduling model based on the results of resources prediction was presented. Finally the simulations on the proposed model and some other models were designed and accomplished. The simulation results demonstrate that the presented model can run both significantly faster and more stable than other models.