Determine a load balancing mechanism for shared resources in a storage system by training a machine learning module

A system, comprising:

• a memory;
• and
• a processor coupled to the memory, wherein the processor performs operations, the operations comprising:
• receiving, by a machine learning module, inputs comprising attributes of a storage controller, wherein the attributes affect allocation of a plurality of resources to a plurality of interfaces in the storage controller;
• in response to a predetermined number of I/O operations occurring in the storage controller, generating, via forward propagation through a plurality of layers of the machine learning module, an output value corresponding to a number of resources to allocate to an interface of the plurality of interfaces in the storage controller;
• calculating a margin of error based on comparing the generated output value to an expected output value, wherein the expected output value is generated from an indication of a predetermined function based at least on a number of I/O operations that are waiting for a resource and a number of available resources;
• and
• adjusting weights of links that interconnect nodes of the plurality of layers via back propagation to reduce the margin of error, to improve a determination of the number of resources to allocate to the interface.

A computer program product, the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform operations in a storage controller or a computational device, the operations comprising:

• receiving, by a machine learning module, inputs comprising attributes of the storage controller, wherein the attributes affect allocation of a plurality of resources to a plurality of interfaces in the storage controller;
• in response to a predetermined number of I/O operations occurring in the storage controller, generating, via forward propagation through a plurality of layers of the machine learning module, an output value corresponding to a number of resources to allocate to an interface of the plurality of interfaces in the storage controller;
• calculating a margin of error based on comparing the generated output value to an expected output value, wherein the expected output value is generated from an indication of a predetermined function based at least on a number of I/O operations that are waiting for a resource and a number of available resources;
• and
• adjusting weights of links that interconnect nodes of the plurality of layers via back propagation to reduce the margin of error, to improve a determination of the number of resources to allocate to the interface.