A multi-objective cloud workflow scheduling method based on improved non-dominated genetic algorithm

A technology of genetic algorithm and scheduling method, applied in the field of multi-objective cloud workflow scheduling based on improved non-dominated genetic algorithm, multi-objective cloud workflow scheduling, can solve complex workflow scheduling and other problems, to improve possibility and accuracy sex, efficiency-enhancing effect

Active Publication Date: 2021-04-13
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Application Information

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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

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  • A multi-objective cloud workflow scheduling method based on improved non-dominated genetic algorithm
  • A multi-objective cloud workflow scheduling method based on improved non-dominated genetic algorithm
  • A multi-objective 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-objective cloud workflow scheduling method based on an improved non-dominated genetic algorithm. By introducing the idea of ​​scoring mechanism, the present invention considers the influence of current population and historical population information on individual dominant information, improves the accuracy of population individual evaluation, and improves the efficiency of iterative search; builds a population hierarchical structure, and intuitively depicts algorithmic search The diversity and optimality of the optimal solutions traversed, by improving the selection method of the parent individuals, and dynamically updating the population hierarchy according to the degree to which the offspring individuals are close to Pareto optimal during the iterative process, the found results are improved. The possibility that the solution is close to the Pareto optimality; at the same time, an adaptive adjustment strategy for the search direction based on the optimal level individual monitoring is proposed. By setting the local optimum and divergence detection parameters, when the search falls into the local optimum or tends to diverge, Adjust the relevant parameters in time, change the direction of optimization to make it jump out of the local optimum or return to 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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
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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