An adaptive scheme for predicting the usage of grid resources

In order to manage the grid resources more effectively and provide a more suitable job scheduling strategy, the prediction information is needed for applications in the Grid computing system, such as the high performance computing and sharing computational resources, etc. In this paper, we propose a prediction system that can predict most information in the grid environment. Whether the repetitive time series pattern of the information exists or not, the proposed system can provide prediction results. We label the environment information in the grid and use the periodicity detector to detect the iterative patterns. The detected patterns can be used to predict several future values. Before the repetitive patterns have been found, a simple scheme that does not require a lot of resource has been used to generate prediction values. A prototype of this model is developed and tested with several test cases. The experimental results by the simulation show that our prediction system is able to capture different kinds of time series patterns and provide accurate prediction for the grid environment.