A method and system for elastic resource expansion based on machine learning

Abstract

The invention discloses a method and system for expanding elastic resources based on machine learning, which belong to the field of cloud computing technology and the field of deep learning, including: d In the case of the calculation amount of the task, the regression model is used to calculate the minimum total amount of resources required to complete the task; the current running status and resource utilization of the task are continuously collected during the running of the task, and the minimum total amount of resources, the current running status of the task, and The resource utilization rate and task calculation amount are input into the prediction model for prediction, and the task completion time T is obtained c ; if T c >t d , then the calculation makes the completion time T of the final task c '<t d The minimum total amount of resources; if the task is not completed, the collection will continue, and if the task is completed, the collection will be stopped. The method of the invention calculates the minimum amount of resources through a regression model to ensure that tasks can be completed on time, predicts the completion time during operation, and automatically performs elastic expansion of computing resources when the completion time exceeds the operation deadline.

Description

technical field [0001] The invention belongs to the field of cloud computing technology and the field of deep learning, and more specifically relates to a method and system for expanding elastic resources based on machine learning. Background technique [0002] The cloud computing business model is generally that the tenant first tells the cloud service provider the number of cloud computing resources that need to be applied, and then the cloud service provider will allocate these resources according to the tenant's request. In this mode, tenants need to estimate the total amount of resources needed according to their own business. However, because cloud tenants lack the understanding of the underlying implementation of cloud service provider services, it is difficult for them to estimate the amount of computing resources required in the virtual environment of the cloud platform based on their previous experience of running business locally. Therefore, the tenant proposes a...

AI Technical Summary

Problems solved by technology

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for expanding elastic resources based on machine learning, thereby solving the technical problems of difficult elastic resource management and low estimation accuracy in the prior art

Images