Data Aggregation based Adaptive Long term load Prediction mechanism in Grid environment

In recent years, as a popular technique to support CSCW, Grid computing is becoming more and more attractive. Hereinto, as the CPU load information can guide task scheduling process greatly, the long-term CPU load prediction becomes a very hot research field and has been widely studied. However, as the prediction errors will be accumulated gradually and meanwhile the relevant parameters´ optimal values may change dynamically with the variance of load series, the previous prediction algorithms usually can not obtain good prediction accuracy when the length of prediction interval is quite large. To address these feature, a Data Aggregation based Adaptive Long term load Prediction mechanism called DA²LP is proposed in this paper. Therein, in order to reduce the number of prediction step and increase the amount of useful input load information, the data aggregation concept is introduced to integrate with AR model. Meanwhile, with the observation and analysis of the relevant parameters´ impact on prediction accuracy in our prediction model, an adaptive parameter selection mechanism is proposed, where the optimal relevant parameters can be adapted automatically to enhance prediction accuracy during the prediction process. The experiments show that our proposed mechanism can outperform significantly the previous prediction methods in mean square error (MSE) for long term load prediction.