Attribute selection method and apparatus, electronic device, and storage medium

By initializing multiple populations and selecting the simulation node with the highest fitness, the problems of low resource utilization and high operating costs in cloud computing are solved, achieving efficient resource allocation and cost optimization.

CN117768397BActive Publication Date: 2026-07-10GUANGZHOU AUTOMOBILE GROUP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUANGZHOU AUTOMOBILE GROUP CO LTD
Filing Date
2023-11-16
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

In cloud computing infrastructure, the allocation of virtual machine resources suffers from low resource utilization and high operating costs. This is mainly due to conflicts between different optimization objectives, which make it difficult to optimize resource waste and load balancing simultaneously.

Method used

By initializing multiple populations, each containing multiple simulation nodes, determining the fitness of each simulation node, and selecting the key attributes of the population to which the simulation node with the highest fitness belongs as input to the resource allocation algorithm, efficient resource allocation is achieved.

Benefits of technology

It improved resource utilization, reduced operating costs, and optimized the resource allocation efficiency of cloud computing infrastructure.

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Abstract

Embodiments of the present application provide an attribute selection method and device, electronic equipment and storage medium. The method comprises: initializing a plurality of populations, each population comprising a plurality of simulation nodes; determining the fitness of each simulation node according to the values of delay, bandwidth, throughput, computing resources, storage resources, network resources and load balancing indicators contained in each simulation node, and a preset fitness function; determining the simulation node with the maximum fitness from the plurality of populations based on the fitness of each simulation node; taking the key attribute corresponding to the population to which the simulation node with the maximum fitness belongs as a target attribute, the target attribute being an input of a resource allocation algorithm in an actual application process, and obtaining a resource allocation result. The key attribute of the population to which the simulation node with the maximum fitness belongs is taken as an input of a resource allocation algorithm in an actual application, so that resource allocation is performed in the actual application through the key attribute and the resource allocation algorithm, and resource utilization efficiency is improved.
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