Predicting Job Wait Time in Grid Environment by Applying Machine Learning Methods on Historical Information
To have high performance scheduling mechanisms in grid computing, the authors need accurate methods for estimating parameters like jobs' wait time and run time. In this paper, they consider wait time prediction problem. Different regression techniques are examined on AuverGrid data set to predict wait time. To improve the quality of prediction, some extra features are proposed. Simulation results show that adding these features reduces prediction error between 13% and 60% in different methods. Results also show that K-nearest neighbor outperforms other regression techniques. They have compared the k-nearest neighbor method in both original and enriched data set with Last-M. K-Nearest neighbor in enriched data set outperforms both Last-M and K-nearest neighbor in original data set in accuracy and perfect prediction percentage.