Elastic expansion method based on load regression prediction in cloud environment and readable storage medium

Abstract

The invention discloses an elastic scaling method based on load regression prediction in a cloud environment and a readable storage medium, belongs to the field of elastic scaling of computing resources, and aims to solve the technical problem of how to predict the user load in the next time period and reasonably adjust the computing resources of a user according to the load. The method comprisesthe following steps: acquiring historical data of normal operation of a user service in a period of time, and constructing a training data set; based on the training data set, training a regression model, calculating the load of the next moment through the trained regression model, training a linear model between the calculation resource usage amount and the load, and calculating the calculation resource usage amount of the next moment through the trained linear model; and optimizing the user cost by taking the computing resource usage amount at the next moment as a limiting condition to obtain a computing resource configuration target solution set, rearranging the computing resource configuration target solution set and the computing resource configuration current solution set, calculating to obtain a difference, and selecting a proper horizontal telescopic mode or vertical telescopic mode based on the difference.

Description

technical field [0001] The invention relates to the field of elastic scaling of computing resources, in particular to an elastic scaling method based on load regression prediction in a cloud environment and a readable storage medium. Background technique [0002] With the maturity and development of cloud computing technology, there are more and more manufacturers providing various public cloud services on the market, and more and more enterprises and users also choose to migrate and deploy their application services to the cloud, using public cloud vendors to provide The resources and services of computing resources reduce the deployment, operation and maintenance, management and network protection costs of computing resources. Users can pay on demand and choose their own resource usage reasonably. At the same time, they can purchase and delete computing resources at any time according to business load requirements, thereby greatly saving their own technical and capital cos...

AI Technical Summary

Problems solved by technology

However, the scaling methods provided by various vendors are the horizontal expansion mode with the same computing resource configuration. This coarse-grained scaling activity cannot flexibly adapt to changes in the load of user service products. Although this reduces the difficulty of application creation and deployment, it still It will cause a certain waste of resources, and it is impossible to control the user's resource usage cost well

Images