Resource configuration method and apparatus forheterogeneous cloud services

A resource configuration apparatus for heterogeneous cloud services, comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• establishing a basic model with a general structure for at least two heterogeneous cloud services, wherein the basic model comprises a trend model and a periodic model;
• determining a cloud service in the at least two cloud services as a target cloud service, and acquiring a target historical data set of the target cloud service;
• training the trend model and the periodic model using the target historical data set, to generate a trained trend model and a trained periodic model;
• generating a target prediction model corresponding to the target cloud service based on the trained trend model and the trained periodic model;
• and
• generating, based on the target prediction model, a resource amount demanded by the target cloud service in a future time period, and configuring resources for the target cloud service according to the demanded resource amount.

A non-transitory computer-readable storage medium, storing a computer program thereon, wherein the computer program, when executed by a processor, causes the processor to perform operations, the operations comprise:

• establishing a basic model with a general structure for at least two heterogeneous cloud services, wherein the basic model comprises a trend model and a periodic model;
• determining a cloud service in the at least two cloud services as a target cloud service, and acquiring a target historical data set of the target cloud service;
• training the trend model and the periodic model using the target historical data set, to generate a trained trend model and a trained periodic model;
• generating a target prediction model corresponding to the target cloud service based on the trained trend model and the trained periodic model;
• and
• generating, based on the target prediction model, a resource amount demanded by the target cloud service in a future time period, and configuring resources for the target cloud service according to the demanded resource amount.