Resource scheduling method based on elastic block storage service, device, and storage medium

An electronic device, comprising:

• at least one processor and at least one memory,
• wherein the at least one memory stores a computer-executable instruction;
• and
• the at least one processor executes the computer-executable instruction stored in the at least one memory, to enable the at least one processor to perform a resource scheduling method based on an elastic block storage service, and the method comprises:
• acquiring attribute information and operation event information of each cloud disk of a first tenant in a tenant set;
• generating a candidate cloud disk behavior label set of the first tenant based on the attribute information and the operation event information of each cloud disk;
• determining a current weight of each candidate cloud disk behavior label in the candidate cloud disk behavior label set based on a cloud disk behavior change of the first tenant and cloud disk behavior changes of other tenants in the tenant set other than the first tenant, and determining a first cloud disk behavior label of the first tenant from the candidate cloud disk behavior label set based on the current weight of each candidate cloud disk behavior label;
• generating labeled feature information of the first tenant based on the first cloud disk behavior label;
• and
• scheduling a cloud disk of the first tenant based on the labeled feature information of the first tenant.

A non-transitory computer-readable storage medium, storing a computer-executable instruction, wherein the non-transitory computer-executable instruction, when executed by a processor, implements a resource scheduling method based on an elastic block storage service, and the method comprises:

• acquiring attribute information and operation event information of each cloud disk of a first tenant in a tenant set;
• generating a candidate cloud disk behavior label set of the first tenant based on the attribute information and the operation event information of each cloud disk;
• determining a current weight of each candidate cloud disk behavior label in the candidate cloud disk behavior label set based on a cloud disk behavior change of the first tenant and cloud disk behavior changes of other tenants in the tenant set other than the first tenant, and determining a first cloud disk behavior label of the first tenant from the candidate cloud disk behavior label set based on the current weight of each candidate cloud disk behavior label;
• generating labeled feature information of the first tenant based on the first cloud disk behavior label;
• and
• scheduling a cloud disk of the first tenant based on the labeled feature information of the first tenant.