An approach to grid scheduling optimization based on fuzzy association rule mining

This paper presents a grid scheduling optimization technique based on knowledge discovery. The main idea is to transform the grid monitoring data into a performance data set, extract the association patterns of performance data through fuzzy association rule mining, then construct optimization logic according to the mining results, and finally optimize the grid scheduling. In the process of data mining, a method of association rule mining is proposed based on time-window and fuzzy set concepts, which can mine data for quantitative attribute value based on the attribute and time dimensions in grid performance data set