Predictive System for Optimizing API Behaviors

A computer system, comprising:

• an Application Programing Interface (API) gateway that comprises:
• one or more processors;
• and
• one or more computer-readable non-transitory storage media coupled to the one or more processors that stores instructions operable when executed by the one or more processors to cause the system to perform a method comprising:
• collecting properties associated with each of a plurality of paths between at least one device and an Application Programing Interface (API) gateway, wherein the at least one device comprises at least one endpoint device;
• monitoring the properties associated with each of the plurality of paths to determine a current level of performance for each of the plurality of paths;
• monitoring the at least one device to determine a current load of the at least one device;
• analyzing, using machine learning, the current level of performance for each of the plurality of paths and the current load of the at least one device to produce a predictive analytics model for congestion on each of the paths;
• determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the API gateway, the plurality of paths, and the at least one device based on at least the current level of performance for each of the plurality of paths and the current load of the at least one device;
• and
• performing the corrective action when the predictive analytics model indicates that the corrective action is needed.

At least one non-transitory computer-readable storage medium having stored therein instructions which, when executed by one or more processors, cause the one or more processors to:

• collect properties associated with each of a plurality of paths between at least one device and an Application Programing Interface (API) gateway, wherein the at least one device comprises at least an endpoint device;
• monitoring the properties associated with each of the plurality of paths to determine a current level of performance for each of the plurality of paths;
• monitoring the at least one device to determine a current load of the at least one device;
• analyzing, using machine learning, the current level of performance for each of the plurality of paths and the current load of the at least one device to produce a predictive analytics model for congestion on each of the paths;
• determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the API gateway, the plurality of paths, and the at least one device based on at least the current level of performance for each of the plurality of paths and the current load of the at least one device;
• and
• performing the corrective action when the predictive analytics model indicates that the corrective action is needed.