A scheduler for a storage node uses multi-dimensional weighted resource cost matrices to schedule processing of IOs. A separate matrix is created for each computing node of the storage node via machine learning or regression analysis. Each matrix includes distinct dimensions for each emulation of the computing node for which the matrix is created. Each dimension includes modeled costs in terms of amounts of resources of various types required to process an IO of various IO types. An IO received from a host by a computing node is not scheduled for processing by that computing node unless enough resources are available at each emulation of that computing node. If enough resources are unavailable at an emulation, then the IO is forwarded to a different computing node that has enough resources at each of its emulations. A weighted resource cost for processing the IO is calculated and used to determine scheduling priority. The weights or regression coefficients from the model may be used to calculate weighted resource cost.
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