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Multi-target cloud workflow scheduling method based on improved non-dominated genetic algorithm

A technology of genetic algorithm and scheduling method, applied in genetic rules, computing, resource allocation, etc., can solve problems such as complex workflow scheduling, and achieve the effect of improving possibility, efficiency, and accuracy

Active Publication Date: 2019-07-09
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0005] The purpose of the present invention is to propose a multi-objective cloud workflow scheduling method based on improved non-dominated genetic algorithm in order to solve the complex workflow scheduling problem in the cloud environment

Method used

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  • Multi-target cloud workflow scheduling method based on improved non-dominated genetic algorithm
  • Multi-target cloud workflow scheduling method based on improved non-dominated genetic algorithm
  • Multi-target cloud workflow scheduling method based on improved non-dominated genetic algorithm

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Embodiment Construction

[0020] The method of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0021] The two specific optimization objectives considered in this method are the total execution time and cost of the entire workflow. Now, the simple cloud workflow scheduling problem with 12 subtasks in the workflow, 4 virtual machines and 6 individuals in the population is given as For example, the specific implementation of the method of the present invention is described in detail. Then, aiming at more complex workflow scheduling problems, the scheduling performance of the method of the present invention is further tested.

[0022] A multi-objective cloud workflow scheduling method based on improved non-dominated genetic algorithm, such as figure 1 shown, including the following steps:

[0023] Step 1. Population initialization. That is, each chromosome is initialized to form an initial population.

[0024] Wherein, the chromosome init...

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Abstract

The invention discloses a multi-target cloud workflow scheduling method based on an improved non-dominated genetic algorithm. By introducing a scoring mechanism idea and considering the influence of the current population and historical population information on individual dominant information, the accuracy of population individual evaluation is improved, and the efficiency of iterative search isimproved. The method comprises the following steps of constructing population hierarchy so as to directly depict diversity and optimality of an optimal solution traversed by algorithm search, and dynamically updating a population hierarchical structure according to the degree of approaching Pareto optimality of an offspring individual in an iteration process by improving a parent individual selection mode, so that the possibility that the found solution approaches Pareto optimality is improved. Meanwhile, a search direction self-adaptive adjustment strategy based on optimal level individual monitoring is provided.By setting local optimum and divergence detection parameters, relevant parameters can be adjusted in time when the search is trapped in the local optimum or tends to be divergent,and the optimization direction is changed to jump out of the local optimum or regression convergence.

Description

technical field [0001] The invention relates to a multi-objective cloud workflow scheduling method, in particular to a multi-objective cloud workflow scheduling method based on an improved non-dominated genetic algorithm, and belongs to the technical field of cloud computing. Background technique [0002] Cloud computing is a paradigm of distributed system computing. It has many advantages such as rapid provision of resources, pay-per-use, and elastic expansion on demand. It provides an economical and efficient management and automatic operation environment for scientific applications. Many scientists use workflows to build their complex applications and deploy them on cloud platforms for execution. Workflow can be modeled as a directed acyclic graph composed of multiple tasks connected according to data and control flow dependencies, where "vertices" represent tasks, and "edges" represent data or control dependencies between tasks. [0003] Often, complex scientific workfl...

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Application Information

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IPC IPC(8): G06F9/455G06F9/50G06N3/12
CPCG06F9/45558G06F9/5027G06F2009/4557G06N3/126
Inventor 王彬阳李慧芳石其松胡光政邹伟东柴森春夏元清
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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