Patent ID: 11971793
Assignee: MICRO FOCUS LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. A computing system comprising:
physical resources, including a storage device storing a database of a database management system (DBMS), a processor, and memory;
query monitoring logic implemented by the physical resources to dynamically monitor how many queries are concurrently being executed by the DBMS against the database, to maintain a count of the queries concurrently being executed;
resource monitoring logic implemented by the physical resources to dynamically monitor current physical resources utilization of the physical resources of the computing system as a whole and not on a per-query basis such that the current physical resources utilization reflects all activity of the computing system, including the queries concurrently being executed as well as other activity of the computing system; and
DBMS logic implemented using the physical resources to:
receive a plurality of query-based statistics for each operator of a plurality of operators of a query plan fora received query to be executed against the database, the query plan comprising a hierarchical tree of the plurality of operators that are executable in a bottom-up manner to execute the received query, wherein during generation of the query plan the query-based statistics for the received query are generated for each operator of the plurality of operators of the hierarchical tree of the query plan with respect to the received query in isolation and without taking into account the queries concurrently being executed;
provide an input vector to a machine-learning model, the input vector including each of only three types of input features;
the current physical resources utilization of the computing system as a whole and not on a per-query basis, as a first type of input feature;
the count of the queries concurrently being executed, as a second type of input feature; and
the query-based statistics for each operator of the query plan and generated during generation of the query plan, as a third type of input feature;

receive as output from the machine-learning model an estimated execution time of the received query, the machine-learning model using each of the three types of input features included in the input vector provided to the machine-learning model to dynamically predict the estimated execution time; and
cause the DBMS to execute the received query against the database, by executing the operators of the query plane based on the estimated execution time for the received query.