A cloud workflow scheduling method and device based on multi-objective optimization

A multi-objective optimization and workflow technology, applied in the field of high-performance computing, can solve problems such as poor diversity, large amount of calculation and poor convergence of cloud workflow scheduling methods

Active Publication Date: 2019-02-01
西安电子科技大学昆山创新研究院 +5
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  • Application Information

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Problems solved by technology

[0005] In the prior art, the diversity of solutions obtained by cloud workflow scheduling methods based on Pareto domination and cloud workflow scheduling methods based on evolutionary m

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  • A cloud workflow scheduling method and device based on multi-objective optimization
  • A cloud workflow scheduling method and device based on multi-objective optimization
  • A cloud workflow scheduling method and device based on multi-objective optimization

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Embodiment Construction

[0069] In order to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0070] It should be clear that the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0071] Terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. As used in the embodiments of the present invention and the appended claims, the singular forms "a", "said" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0072] It should be understood that although the...

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Abstract

The embodiment of the invention provides a cloud workflow scheduling method and device based on multi-objective optimization, which relate to the technical field of high-performance computing, can carry out multi-objective optimization based on local search and weight vector adjustment, and obtain faster convergence speed and better diversity of individuals at the same time. The method includes establishing a simulation cloud computing resource model and a cloud workflow task model; then the population is initialized and the fitness of each individual is calculated. Further evolutionary processing for each individual; then, the local search is carried out to obtain two optimal individuals in the neighbors of each sub-problem, and the new individuals are calculated to update the individualsagain. And when the updated individual satisfies the convergence condition, the evolutionary population is adjusted by weight vector, which includes deleting subproblems of congested regions and adding new subproblems to sparse regions; finally outputting optimal individual. The technical proposal provided by the embodiment of the invention is applicable to a cloud workflow scheduling process.

Description

【Technical field】 [0001] The invention relates to the technical field of high-performance computing, in particular to a multi-objective optimization-based cloud workflow scheduling method and device. 【Background technique】 [0002] With the development of high-performance computing technology, more and more researchers are gradually applying multi-objective optimization technology to cloud workflow scheduling problems. Multi-objective optimization technology makes it possible for users to make preference decisions from multiple optimal solutions. For multi-objective optimized cloud workflow scheduling, there is no certain decision that can optimize all objectives at the same time, but a set of compromise Pareto decisions with multiple conflicting objectives can be obtained. The real Pareto decision set of cloud workflow scheduling is difficult to obtain. It is often not necessary to obtain a real Pareto decision set, usually a set of Pareto asymptotically optimal decisions ...

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Application Information

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IPC IPC(8): G06F9/50G06N3/00G06Q10/04
CPCG06F9/5027G06N3/006G06Q10/04
Inventor 马玉新于聚省郭浩东戚玉涛鲍亮
Owner 西安电子科技大学昆山创新研究院
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