Container quantity adjustment for application

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• in response to determining that a container quantity of an application at each of a plurality of historical moments changes periodically, determining historical data of the application, wherein the historical data comprise traffic of the application at each of the plurality of historical moments;
• predicting traffic distribution of the application within a predetermined duration based on the historical data by using a pre-trained traffic prediction model;
• predicting container quantity distribution of the application within the predetermined duration based on the traffic distribution and a predetermined target utilization of a container by using a pre-trained quantity prediction model;
• and
• adjusting a container quantity of the application at each of a plurality of predetermined moments within the predetermined duration based on the container quantity distribution.

A computer-implemented system, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
• in response to determining that a container quantity of an application at each of a plurality of historical moments changes periodically, determining historical data of the application, wherein the historical data comprise traffic of the application at each of the plurality of historical moments;
• predicting traffic distribution of the application within a predetermined duration based on the historical data by using a pre-trained traffic prediction model;
• predicting container quantity distribution of the application within the predetermined duration based on the traffic distribution and a predetermined target utilization of a container by using a pre-trained quantity prediction model;
• and
• adjusting a container quantity of the application at each of a plurality of predetermined moments within the predetermined duration based on the container quantity distribution.