Discriminative Model for Google Host Load Prediction with Rich Feature Set

Host load prediction is one of the key research issues in Cloud computing. However, due to the drastic fluctuation of the host load in the Cloud, accurately predicting the host load remains a challenge. In this paper, a discriminative model (SVM) is employed to improve upon the accuracy of host load prediction in a Cloud data center. A rich set of features are generated by function based methods and incorporated into discriminative modelling. The performance of our proposed method is empirically evaluated using a one-month trace of a Google data center with over 12000 heterogeneous hosts. The results show that the proposed method achieves a better prediction performance than some state-of-the-art methods.