Database control using machine learning based prediction

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured to implement the following steps:
• accessing a first set of designated database metrics characterizing one or more active sessions of at least one database workload for one or more designated time intervals, wherein the one or more active sessions utilize one or more database resources for performing one or more database operations;
• accessing a second set of designated database metrics characterizing one or more waiting sessions of the at least one database workload for the one or more designated time intervals, wherein the one or more waiting sessions execute in response to an occurrence of one or more designated events, wherein the second set of designated database metrics is distinct from the first set of designated database metrics;
• evaluating, by at least one processing device, an amount of work performed by the one or more active sessions and the one or more waiting sessions of the at least one database workload within at least one designated time interval by aggregating the first and second sets of designated database metrics for within each of the at least one designated time interval;
• applying, by the at least one processing device, historical workload data of the at least one database workload to one or more machine learning models to obtain a forecast of an amount of work performed by the at least one database workload for one or more designated time intervals within at least one subsequent designated time period;
• and
• initiating, by the at least one processing device, at least one automated action using the forecast of the amount of work performed by the at least one database workload for the one or more designated time intervals, wherein the at least one automated action comprises rescheduling an execution of one or more scheduled jobs of the at least one database workload based at least in part on the obtained forecast of the amount of work performed by the at least one database workload for the one or more designated time intervals within the at least one subsequent designated time period.

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

• accessing a first set of designated database metrics characterizing one or more active sessions of at least one database workload for one or more designated time intervals, wherein the one or more active sessions utilize one or more database resources for performing one or more database operations;
• accessing a second set of designated database metrics characterizing one or more waiting sessions of the at least one database workload for the one or more designated time intervals, wherein the one or more waiting sessions execute in response to an occurrence of one or more designated events, wherein the second set of designated database metrics is distinct from the first set of designated database metrics;
• evaluating, by at least one processing device, an amount of work performed by the one or more active sessions and the one or more waiting sessions of the at least one database workload within at least one designated time interval by aggregating the first and second sets of designated database metrics for within each of the at least one designated time interval;
• applying, by the at least one processing device, historical workload data of the at least one database workload to one or more machine learning models to obtain a forecast of an amount of work performed by the at least one database workload for one or more designated time intervals within at least one subsequent designated time period;
• and
• initiating, by the at least one processing device, at least one automated action using the forecast of the amount of work performed by the at least one database workload for the one or more designated time intervals, wherein the at least one automated action comprises rescheduling an execution of one or more scheduled jobs of the at least one database workload based at least in part on the obtained forecast of the amount of work performed by the at least one database workload for the one or more designated time intervals within the at least one subsequent designated time period.