problem of choosing right statistical method for scheduler prediction
Do you know if the scheduler has a memory?

Let us assume for a moment that the scheduler has no memory.  This is a straightforward classification (supervised learning) problem: the inputs are X, the outputs are the schedules (N->M maps).  Actually, if every N gets scheduled and the only question is which M it gets, the outputs are lists which channel (or none) is scheduled to each block, and there is only a certain possible number of those, so you can model them as discrete outputs (classes) with their own probabilities.  Use whatever you like (AdaBoost, Naive Bayes, RBF SVM, Random Forest...) as a classifier.  I think you will quickly learn about the general behavior of the scheduler.

If the scheduler has a memory, then things get complicated.  I think you might approach that as a hidden Markov model: but the number of individual states may be quite large, and so it may be essentially impossible to build a complete map of transition probabilities.