Abstract: A method, apparatus and computer program product for estimating resource consumption for steps in a query execution plan for a query performed by a relational database management system (RDBMS) in a computer system. Past execution data for the steps are used to train a machine learning (ML) model and its model parameters to predict execution times for the steps. A prediction module comprised of the ML model configured by the model parameters predicts an execution time for a current step of the query execution plan for the query, based on current step information and current system load. A boosting module boosts the current step either up or down for processing by the RDBMS to meet a service level goal (SLG) for the query, based on the predicted execution time for the current step, as well as an elapsed query time, a query SLG time, and/or a query CPU time.