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This article focuses on mapping jobs to resources with use of off-the-shelf machine learning methods. The machine learning methods are used in the black-box manner, having a wide variety of parameters for internal cross validation. In the article we focus on two sets of experiments, both constructed with a goal of congesting the system. In the first part, the machine learning methods are used as assistants for the base resource selection algorithms, while in the second part, they are used to directly select the resource for job execution. We set up two experiments with different objectives. In the first experiment, the objective was to maximize the number of jobs finished within the deadline, while in the second experiment, the objective was to minimize the job tardiness. Finally, in the first part we were changing resource selections of simple job dispatching algorithms, while in the second part, we were employing the optimization scheme of the ALEA algorithm [15]. The results show that even with a such black-box approach of the off-the-shelf machine learning methods, we can achieve improvements or at least get comparable results.