Machine learning based processing of network operations using sequence alignment

A method, comprising:

• identifying, by one or more processors coupled with memory, from a plurality of sequences, a sequence of functions to perform an action on a transaction processing system, the identification is based on a constraint on an order of functions within the sequence of functions;
• identifying, by the one or more processors, one or more machine learning (ML) models trained on performance data related to execution of a plurality of actions using sequences of functions and according to a plurality of constraints for the plurality of actions;
• determining, by the one or more processors, using the one or more ML models, a likelihood that the sequence of functions performs the action within a performance tolerance and according to the constraint;
• providing, by the one or more processors, responsive to the likelihood satisfying a threshold, an instruction to the transaction processing system to cause the transaction processing system to perform the action using the sequence of functions.

A non-transitory computer-readable media having processor readable instructions, such that, when executed, cause at least one processor to:

• identify, from a plurality of sequences, a sequence of functions to perform an action on a transaction processing system;
• identify, for the action, a constraint on an order of functions within the sequence of functions;
• identify one or more machine learning (ML) models trained on performance data related to execution of a plurality of actions using sequences of functions;
• determine, using the one or more ML models, a likelihood that the sequence of functions performs the action within a performance tolerance when executed according to the constraint on the order of functions;
• provide, responsive to the likelihood satisfying a threshold, an instruction to the transaction processing system to cause the transaction processing system to perform the action using the sequence of functions.