Data loading based on workload predictions for improved performance of cloud-based systems

A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for selectively loading data to low-latency memory, the operations comprising:

• receiving a workload period representing a period of time, during which a workload is applied to a database system;
• providing a set of machine learning (ML) models based on historical data representative of multiple executions of the workload over historical instances of the workload period, each ML model configured to predict a cluster arrival rate curve (cARC);
• and
• during execution of the workload period and, for each timeslice of a plurality of timeslice of the workload period:
• providing a predicted cARC from each ML model, the predicted cARC representative of a predicted workload for a respective timeslice,
• determining column visiting times for each of a plurality of columns of each of a plurality of tables stored in the database system,
• generating a column list based on the column visiting times, and
• loading column data representative of columns included in the column list into low-latency memory prior to execution of a workload during the respective timeslice.

A system, comprising:

• a computing device;
• and
• a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for selectively loading data to low-latency memory, the operations comprising:
• receiving a workload period representing a period of time, during which a workload is applied to a database system;
• providing a set of machine learning (ML) models based on historical data representative of multiple executions of the workload over historical instances of the workload period, each ML model configured to predict a cluster arrival rate curve (cARC);
• and
• during execution of the workload period and, for each timeslice of a plurality of timeslice of the workload period:
• providing a predicted cARC from each ML model, the predicted cARC representative of a predicted workload for a respective timeslice,
• determining column visiting times for each of a plurality of columns of each of a plurality of tables stored in the database system,
• generating a column list based on the column visiting times, and
• loading column data representative of columns included in the column list into low-latency memory prior to execution of a workload during the respective timeslice.