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Web application adaptive resource allocation method based on machine learning

A machine learning and resource allocation technology, applied in the field of self-learning and self-adaptive allocation, can solve problems such as real-time decision-making of resource scheduling, and achieve the effect of reducing the search space

Active Publication Date: 2019-01-08
FUZHOU UNIV
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

Traditionally, it is necessary to rely on manual intervention to give corresponding knowledge and strategies, which makes it difficult to make real-time decisions on resource scheduling online

Method used

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  • Web application adaptive resource allocation method based on machine learning
  • Web application adaptive resource allocation method based on machine learning
  • Web application adaptive resource allocation method based on machine learning

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Embodiment Construction

[0040] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0041] The present invention provides a machine learning-based Web application self-adaptive resource configuration method, which includes the following steps: Step S1: Virtualize the operating system of the physical machine and divide it into two virtual machines, and the two virtual machines run their respective operating systems ; Step S2: Propose two machine learning models for the above two virtual machines; use the prediction model of response time based on machine learning to predict the response time under a given environmental change; Step S3: use the online decision-making mechanism based on genetic algorithm According to the above model, formulate the fitness function of the response to search for the allocation scheme of software and hardware resources with optimal response time.

[0042] The change of the environment of the Web app...

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Abstract

The invention provides a Web application adaptive resource allocation method based on machine learning, which comprises the following steps: step S1, the physical machine operating system is virtualized and divided into two virtual machines, and the two virtual machines run their respective operating systems respectively; step S2, two machine learning models are provided for the two virtual machines respectively; a response time prediction model based on machine learning is used to predict the response time of a given environment; step S3, based on the online decision-making mechanism based onthe genetic algorithm, the fitness function of the response is formulated according to the above model to search for the software and hardware resource allocation scheme with the optimal response time. The method can train the prediction model from the historical data and automatically allocate the software and hardware resources for the Web application service according to the genetic algorithm.

Description

technical field [0001] The invention belongs to the field of software engineering cloud computing, and in particular relates to a self-learning and self-adaptive distribution method of software and hardware resources in a cloud environment. Background technique [0002] As a guiding methodology based on the Internet, cloud computing can enter the resource sharing pool and allocate resources reasonably so that these resources can be quickly provided and properly used. In engineering practice, the present invention often needs to consider factors such as the rapid change and continuous growth of the number of Web application workloads, the situation of network resource utilization, server status, etc., and adjust the server memory allocation ratio of the Web application system in time to handle the current request volume , to avoid paralysis of the system due to excessive load and deployment scale not suitable for current needs. However, if the present invention always mainta...

Claims

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Application Information

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IPC IPC(8): G06F9/455G06F9/50G06K9/62G06N3/12
CPCG06F9/45558G06F9/505G06N3/126G06F2009/4557G06F18/2411
Inventor 陈星朱芳宁林俊鑫陈佳晴
Owner FUZHOU UNIV
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