1. A computer-implemented method, comprising: accessing cost data describing costs of executing a plurality of data queries on one or more data storage systems, the costs indicating consumption of processing resources during execution of the plurality of data queries; accessing execution time data describing previous execution times of the plurality of data queries; generating a preliminary model based on a linear regression analysis that correlates the costs and the previous execution times of the plurality of data queries; determining a variance that characterizes a distribution, relative to the preliminary model, of pairs of the cost data and the execution time data associated with individual executions of the plurality of data queries; identifying a subset of the pairs of the cost data and the execution time data, the subset omitting one or more of the pairs of the cost data and the execution time data that are outside a multiplicative factor times the variance; generating a model based on the linear regression analysis that provides a first predicted execution time as a function of a cost of a data query; executing the data query on the one or more data storage systems; determining that the data query exceeds a threshold execution time while executing on the one or more data storage systems; based on the determination, employing the model to calculate a second predicted execution time of the data query based on the cost of the data query; and based at least partly on the second predicted execution time, determining a time period for executing the data query on the one or more data storage systems such that the data query is predicted to complete within the time period, wherein the time period includes a sum of the second predicted execution time and a multiplicative factor times the variance that characterizes a distribution of a combined query cost and execution time data, relative to the model.