Grid scheduling performance is significantly affected by the accuracy of job runtime estimation. Since past performance is a good indicator of future trends, we use a case-based reasoning approach to predict the execution time, or run time, based on past experience. We first define the similarity of jobs and similarity of machines, and then determine which job and machine characteristics affect the run time the most by analyzing information from previous runs. We then create a case base to store historical data, and use the
case-based reasoning system to fetch all relevant cases from the case base. We apply this approach to schedule Functional Regression Tests for IBM
(DB2 UDB) products. The results show that our approach achieves low runtime estimation errors and substantially improves grid scheduling performance.