System and method for intelligent real-time listening and load balancing of integration process executions

1. An information handling system operating an intelligent real time listen and load balance system comprising:

a processor receiving a customized data business integration process modeled via a graphical user interface with icons and connectors for executing one or more triggered business integration processes in response to triggering events;

the processor training a triggering event correlating neural network for an intelligent listener API to poll a repository of information tracking changes made to datasets to identify a correlation between previous changes made to a dataset during previous triggering events for a triggered integration process and previous executions of the triggered integration process, based on previous co-occurrences of the previous triggering events and the previous executions of the triggered integration process;

the processor determining, based on updated received metadata associated with a current triggering event input into a trained triggering event correlating neural network, that current changes to the dataset during the current triggering event correlates to the triggered integration process, indicating new or modified data requires execution of the triggered integration process;

the processor determining predicted triggered integration process execution metrics for a plurality of cloud computing nodes by inputting received performance metrics for a plurality of cloud computing nodes into training an execution location optimizing neural network for the triggered integration process;

the processor identifying an optimal one of the plurality of cloud computing nodes by comparing the predicted triggered integration process execution metrics for the plurality of cloud computing nodes, based on preset performance requirements; and

a network interface device transmitting code sets and a runtime engine for automatic execution of the triggered integration process to the optimal cloud computing node.

2. The information handling system of claim 1 , wherein performance metrics describing current operating conditions for the plurality of cloud computing nodes include a measurement of dropped packets during a preset period of time during execution of the triggered integration process.

3. The information handling system of claim 1 , wherein predicted triggered integration process execution metrics include failure to complete execution of the triggered integration process.

4. The information handling system of claim 1 , wherein predicted triggered integration process execution metrics include time outs occurring prior to completion of execution of the triggered integration process.

5. The information handling system of claim 1 , wherein predicted triggered integration process execution metrics include time to complete execution of the triggered integration process.

6. The information handling system of claim 1 , wherein the predicted triggered integration process execution metrics includes a predicted total time required to complete execution of the triggered integration process.

7. The information handling system of claim 1 further comprising:

a graphical user interface displaying dependence of the triggered integration process upon the current changes made to the dataset during the triggering event through customization of a start visual element representing a portion of the triggered integration process.

8. A method of intelligent, real time load balancing for execution of an integration process comprising:

executing, via processor, a customized data business integration process modeled via a graphical user interface with icons and connectors and including code instructions for executing one or more triggered business integration processes in response to triggering events;

determining, via a processor, by polling a repository of information tracking changes made to datasets that a triggering event for the triggered integration process has occurred based on metadata associated with the triggering event and upon execution logs for previous executions of the triggered integration process;

receiving, via a network interface device, performance metrics describing current operating conditions for a plurality of cloud computing nodes;

determining predicted triggered integration process execution metrics for each of the plurality of cloud computing nodes, describing predicted performance of the triggered integration process at each of the plurality of cloud computing nodes, by inputting the performance metrics for the plurality of cloud computing nodes into an execution location optimizing neural network trained for the triggered integration process;

identifying an optimal cloud computing node by comparing the predicted triggered integration process execution metrics for each of the plurality of cloud computing nodes, based on preset triggered integration process performance requirements; and

transmitting code sets and a runtime engine for automatic execution of the triggered integration process to the optimal cloud computing node.

9. The method of claim 8 , wherein the triggering event is predefined by a user via an integration process modelling graphical user interface for a start shape visual element of the triggered integration process.

10. The method of claim 8 , wherein the triggering event is determined by the processor via a triggering event correlating neural network of an intelligent listener API, based on previous co-occurrences of previous changes made to a dataset during previous triggering events and previous executions of a triggered integration process modeled in part using a start shape visual element identifying the triggering event.

11. The method of claim 8 , wherein performance metrics describing current operating conditions for the plurality of cloud computing nodes include a latency measurement.

12. The method of claim 8 , wherein performance metrics describing current operating conditions for the plurality of cloud computing nodes include a throughput measurement.

13. The method of claim 8 , wherein the predicted triggered integration process execution metrics includes a predicted number of datasets created, read, updated, or deleted over a preset period of time.

14. The method of claim 8 , wherein the predicted triggered integration process execution metrics includes a predicted total time required to complete execution of the triggered integration process.

15. An information handling system operating an intelligent real time listen and load balance system comprising:

a processor receiving a customized data business integration process modeled via a graphical user interface with icons and connectors for executing one or more triggered business integration processes in response to triggering events;

a network interface device receiving a plurality of metadata files describing changes made to a plurality of datasets during a plurality of previous triggering events, and a plurality of execution logs describing previous executions of a triggered integration process modeled using a plurality of visual elements including a start visual element identifying one or more of the plurality of previous triggering events as events triggering execution of the triggered integration process;

a processor training a triggering event correlating neural network to poll a repository of information tracking changes made to datasets and to identify a correlation between a first change made to a first dataset described in one or more of the plurality of metadata files for the previous triggering events and a second change made to a second dataset described in at least one of the plurality of execution logs for previous executions of the triggered integration process, based on previous co-occurrences of the first change made to the first dataset and the second change made to the second dataset;

the processor executing code instructions of the intelligent real time listen and load balance system to set a trained, intelligent listener API to execute the triggered integration process upon determining the first change has been made to the first dataset pursuant to a current triggering event; and

the network interface device transmitting code sets and a runtime engine for execution of the triggered integration process to an execution location.

16. The information handling system of claim 15 , wherein the previous triggering event co-occurs with the previous execution of the triggered integration process when the previous execution of the triggered integration process is prompted by occurrence of the previous triggering event.

17. The information handling system of claim 15 , wherein the plurality of previous executions of the triggered integration process were set by the user to occur each time the previous triggered events occurred.

18. The information handling system of claim 15 , wherein the remote location is preset by a user via an integration process modelling graphical user interface.

19. The information handling system of claim 15 further comprising:

the processor determining an optimal one of a plurality of cloud computing nodes via an execution location optimizing neural network based on predicted triggered integration process execution metrics, for each of a plurality of cloud computing nodes as the execution location of the triggered integration process.

20. The information handling system of claim 15 further comprising:

the processor training the triggering event correlating neural network to identify a plurality of correlations between the triggered integration process and a plurality of datasets changed during a plurality of previous triggering events; and

identifying one of the plurality of datasets changed during the plurality of previous triggering events most closely correlated to the triggered integration process according to the triggering event correlating neural network as the dataset of the previous triggering events correlated to the triggered integration process.