Cloud computing has become very important nowadays for companies, and many of them started offloading job computations instead of increasing the on-premise capacity: the dispatch of those jobs is usually done manually by users, leaving them the choice of the instance and provider to be used. The scope of this thesis is to analyze possible improvements given by the introduction of machine learning in the decision process: the idea was to create a new unit, independent from the scheduler already implemented in the company, having the possibility to extend it to every system deployed. The user is still the author of the choice because he/she is given more information to improve the decision and is not bypassed.