Dynamic task scheduling method and device under cloud computing platform environment

A cloud computing platform and dynamic task technology, which is applied to multi-programming devices, program control devices, resource allocation, etc., can solve problems such as slow convergence speed, and achieve the effect of improving speed and cluster resource utilization.

Inactive Publication Date: 2013-11-27
CHENGDU GKHB INFORMATION TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the present invention provides a dynamic task scheduling method and device in a cloud computing platform environment to solve the problem of slow convergence speed of the hybrid genetic scheduling algorithm in the prior art, and its technical solution is as follows:

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  • Dynamic task scheduling method and device under cloud computing platform environment
  • Dynamic task scheduling method and device under cloud computing platform environment
  • Dynamic task scheduling method and device under cloud computing platform environment

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Embodiment 1

[0061] see figure 1 , is a schematic flowchart of a dynamic task scheduling method in a cloud computing platform environment provided by Embodiment 1 of the present invention. Before performing the method steps provided by this embodiment, the following data are predetermined (assuming that in the cloud computing platform, the number of cluster nodes is C, and the system has m types of tasks in total): the arrival rate of m type tasks {λ i},i=1,2,...m; the expected average service rate of the cloud computing platform for m tasks {μ i},i=1,2,...m; the matrix of the maximum average response times of each node to various tasks {R ij} m·C , i=1,2,...m,j=1,2,...C, the resource ratio matrix {W ij} m·C ,i=1,2,...m,j=1,2,...c.

[0062] Among them, the process of determining the expected average service rate and arrival rate of various tasks on the cloud computing platform includes: using queuing theory to model the task input flow to determine the arrival rate of various tasks; t...

Embodiment 2

[0137] see figure 2 , is a schematic flowchart of a dynamic task scheduling method in a cloud computing platform environment provided by Embodiment 2 of the present invention. Before performing the method steps provided by this embodiment, the following data are predetermined (assuming that in the cloud computing platform, the number of cluster nodes is C, and the system has m types of tasks in total): the arrival rate of m type tasks {λ i},i=1,2,...m; the expected average service rate of the cloud computing platform for m tasks {μ i},i=1,2,...m; the matrix of the maximum average response times of each node to various tasks {R ij} m·C , i=1,2,...m,j=1,2,...C, the resource ratio matrix {W ij} m·C ,i=1,2,...m,j=1,2,...c. After determining the above data, the method provided in the embodiment of the present invention is given, which may include:

[0138] Step S201: Initialize the antibody population to obtain antibody population A n =[A 1 A 2 …A t ], and record the nu...

Embodiment 3

[0167] see Figure 8 , which is a dynamic task scheduling device under a cloud computing platform environment provided by Embodiment 3 of the present invention, the device may include: an initialization module 101, an affinity calculation module 102, a judgment module 103, a first determination module 104, and an antibody cloning module. module 105 , antibody recombination module 106 , antibody variation module 107 and antibody selection module 108 . in:

[0168] The initialization module 101 is used to initialize the antibody population to obtain the antibody population A n =[A 1 A 2 …A t ], and record the number of iterations n as 0, where the antibody population A n Each antibody in represents a configuration scheme, each antibody is encoded as multiple alleles, each allele corresponds to a node, each allele is composed of multiple subsections, and each subsection corresponds to a virtual machine.

[0169] The affinity calculation module 102 is used for determining ...

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Abstract

The invention discloses a dynamic task scheduling method under a cloud computing platform environment. Queuing processing is carried out on tasks by means of a queuing theory at first, quasi-optimal configuration of computing resources distributed by different virtual machines on various nodes in a cluster is searched out by means of an immune clone selection strategy in a manual immunity theory, reasonable configuration is carried out on the computing resources in the cluster, then antibody genes are regulated by means of load balancing, and therefore the configuration of the resources of the cluster can meet requirements for task processing. The invention discloses a dynamic task scheduling device under the cloud computing platform environment, wherein the dynamic task scheduling device comprises an affinity computing module, a judgment module, a determination module and an antibody relation module. According to the dynamic task scheduling method and device under the cloud computing platform environment, dynamic changing and virtualized environments of a cloud platform can be adapted, the optimal configuration is rapidly searched out, and the utilization rate of the resources of the cluster is improved.

Description

technical field [0001] The present invention relates to the field of software technology, in particular to a method and device for dynamic task scheduling in a cloud computing platform environment. Background technique [0002] For heterogeneous distributed systems, the heterogeneity of hardware, operating systems, programming, and communication networks that make up the system leads to great differences in scheduling strategies. There are a large number of task scheduling methods in the prior art, which are mainly divided into Two types: static scheduling and dynamic scheduling. [0003] However, none of the existing scheduling methods has been proven to be optimal, for example, task scheduling methods based on multi-objective optimization genetic algorithm and evolutionary programming, the goal of which is to minimize the scheduling length and maximize the reliability, both of which It is contradictory, and the execution speed and reliability are inversely proportional; b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50G06F9/455G06N3/12
Inventor 吴磊黄廷祝陈鹏刘杰武德安杨镜
Owner CHENGDU GKHB INFORMATION TECH
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