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BP neural network time prediction-based task scheduling method in mixed cloud

A BP neural network and task scheduling technology, applied in the field of task scheduling based on BP neural network time prediction, can solve problems such as poor big data application task scheduling

Active Publication Date: 2018-05-08
WUHAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these algorithms are not well suited for task scheduling of big data applications in a hybrid cloud environment.

Method used

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  • BP neural network time prediction-based task scheduling method in mixed cloud
  • BP neural network time prediction-based task scheduling method in mixed cloud
  • BP neural network time prediction-based task scheduling method in mixed cloud

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Embodiment Construction

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035]The task scheduling method based on BP neural network time prediction in the hybrid cloud proposed by the present invention comprises the following steps:

[0036] 1) Calculate the priority according to the job and QoS requirements submitted by the user, select the job scheduling with high priority, and traverse all tasks in the job in descending order of priority;

[0037] 2) Traverse the private cloud resources in the resource pool, predict the execution time of each task according to the BP neural network model, find out the minimum resource completion time, and update the resource completion time;

[0038] 3) Determine whether the completion time of the resource is less than the deadline of the job, if it is satisfied, assign the task to the private cloud, otherwise add the task to the set MP i , to reset the completion tim...

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Abstract

The invention discloses a BP neural network time prediction-based task scheduling method in mixed cloud. The scheduling method model is formed by three parts: (1) BP neural network time prediction, (2) task scheduling in private cloud and (3) task scheduling in public cloud, wherein in the (1) part, a BP neural network model is trained according to influence factors of Map and Reduce tasks and then executing time of the Map and the Reduce tasks is predicted by the model; in the (2) part, to improve private cloud resource utilization rate and reduce public cloud cost, a task is scheduled firstin the private cloud, resource for earliest completion of the task is calculated according to the predicted task executing time and a task failing to meet the deadline is scheduled to the public cloudresource; and in the (3) part, executing time of the task in the public cloud resource is predicted, and public cloud resource is resolved when deadline is met and minimum cost is required. Task response time is reduced and QoS satisfaction is improved; and maximum private cloud utilization rate and minimum public cloud cost can be obtained.

Description

technical field [0001] The invention relates to the technical field of computer cloud storage, in particular to a task scheduling method based on BP neural network time prediction in a hybrid cloud. Background technique [0002] At present, there are three main types of cloud computing models: private cloud, public cloud and hybrid cloud. Private cloud is a cloud computing model that provides cloud computing services for specific organizations or enterprises. It can be customized according to different construction requirements such as the number and performance of resource nodes. It has good data security, high service quality, and single computing. The characteristics of low cost; the public cloud is in the public network environment (Internet), providing users with rentable services and infrastructure in the form of a third-party service provider, with a large number of nodes, many available resources, and computing usage Billing, sensitivity to communication quality, et...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06N3/08
CPCG06F9/5027G06N3/084Y02D10/00
Inventor 李春林孟川力毛习均杜书猛赵亚辉张鹏
Owner WUHAN UNIV OF TECH
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