Method, apparatus and system for real-time optimization of computer-implemented application operations using machine learning techniques

A computer implemented system for optimizing a runtime configuration of a first plurality of applications hosted at a remote networked environment, the remote networked environment being communicatively coupled to a computer network, the computer network including an Optimizer System, the Optimizer System including a plurality of optimizer algorithms, the system comprising:

• at least one non-transient memory;
• at least one processor operable to execute a plurality of instructions stored in the at least one non-transient memory to automatically:
• cause at least one network device to identify a first application of the first plurality of applications;
• initiate a first measurement of a first operational metric of the first application while the first application is operating in accordance with a first runtime configuration;
• generate first measurement information relating to the first measurement of the first operational metric of the first application;
• calculate, using the first measurement information, a first score in relation to a first optimization objective, the first score being calculated using a first scoring function;
• determine a first set of updated application settings relating to a runtime configuration of the first application;
• deploy the first set of updated application settings at the first application to thereby cause the first application to operate in accordance with a second runtime configuration;
• initiate a second measurement of the first operational metric of the first application while the first application is operating in accordance with the second runtime configuration;
• generate second measurement information relating to the second measurement of the first operational metric of the first application;
• calculate, using the second measurement information, a second score in relation to the first optimization objective, the second score being calculated using a second scoring function;
• compute, using at least the second score, a first reward;
• update at least one of the plurality of optimizer algorithms using information relating to the first reward;
• select, from the plurality of optimizer algorithms, a first optimizer algorithm to be used for determining a second set of updated application settings relating to the runtime configuration of the first application;
• determine, using the first optimizer algorithm, a second set of updated application settings relating to the runtime configuration of the first application;
• deploy the second set of updated application settings at the first application to thereby cause the first application to operate in accordance with a third runtime configuration;
• cause the at least one network device to initiate a third measurement of the first operational metric of the first application while the first application is operating in accordance with the third runtime configuration;
• generate third measurement information relating to the third measurement of the first operational metric of the first application;
• calculate, using the third measurement information, a third score in relation to the first optimization objective, the third score being calculated using a third scoring function;
• compute, using at least the third score, a second reward;
• update at least one of the plurality of optimizer algorithms using information relating to the second reward;
• select, from the plurality of optimizer algorithms, a second optimizer algorithm to be used for determining a third set of updated application settings relating to the runtime configuration of the first application;
• determine, using the second optimizer algorithm, a third set of updated application settings relating to the runtime configuration of the first application;
• and
• deploy the third set of updated application settings at the first application to thereby cause the first application to operate in accordance with a fourth runtime configuration.