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Workflow Optimization Method Based on Partial Order Adaptive Genetic Algorithm in Cloud Computing Environment

A cloud computing environment, genetic algorithm technology, applied in cloud workflow optimization, workflow optimization field based on partial order adaptive genetic algorithm, can solve the lack of resource allocation and task scheduling integrated collaborative optimization method, cloud workflow execution performance The problem is not high, to achieve the effect of improving the overall efficiency, simple decoding, and improving the efficiency of decoding

Active Publication Date: 2022-04-08
ZHEJIANG GONGSHANG UNIVERSITY
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  • Description
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  • Application Information

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Problems solved by technology

[0005] Aiming at the current optimization of cloud workflow execution, usually only from the perspective of resource configuration or task scheduling, the lack of an integrated collaborative optimization method for resource configuration and task scheduling, and the low performance of cloud workflow execution, the present invention provides a cloud computing The workflow optimization method based on partial order adaptive genetic algorithm in the environment effectively improves the execution performance of cloud workflow

Method used

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  • Workflow Optimization Method Based on Partial Order Adaptive Genetic Algorithm in Cloud Computing Environment
  • Workflow Optimization Method Based on Partial Order Adaptive Genetic Algorithm in Cloud Computing Environment
  • Workflow Optimization Method Based on Partial Order Adaptive Genetic Algorithm in Cloud Computing Environment

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Embodiment Construction

[0187] Combine below figure 1 , figure 2 The present invention will be further described in detail with reference to and examples, but the present invention is not limited to the following examples.

[0188] Assume that the cloud computing service provider, that is, the cloud computing environment, has five virtual machine types vm numbered from 1 to 5 1 , vm 2 , vm 3 , vm 4 , vm 5 Available for lease, the computing power, bandwidth, unit time cost, fixed start-up cost, minimum billing time unit, and minimum start-up time of various virtual machine types are shown in Table 1; the timing relationship between a Montage workflow task is as follows figure 2 As shown, there are 15 tasks numbered from 1 to 15 t 1 , t 2 ,...,t 15 The composition, the execution length of each task, the name and length of the input files required for processing and the processed output files are shown in Table 2.

[0189]

[0190]

[0191] Table 1

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[0193] Table 2

[0194...

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Abstract

The invention discloses a workflow optimization method based on a partial order self-adaptive genetic algorithm in a cloud computing environment. Fitness value; cross-mutation operation is performed on the contemporary population to form a new population; a new contemporary population is formed from the contemporary population and the new population; until the termination condition is met, the optimization result is output; the present invention uses the initial individual generation and adaptation based on the level and benefit ratio Methods and strategies such as genetic operations, topological sorting, non-decreasing partial order coding, serial individual decoding based on insertion mode, forward-backward individual decoding improvement, etc., realize integrated collaborative optimization of resource allocation and task scheduling, and improve the efficiency of the entire algorithm Optimizing ability and search efficiency.

Description

technical field [0001] The present invention relates to the fields of computer technology, information technology and system engineering, in particular to a cloud workflow optimization method, and more specifically, to a workflow optimization method based on a partial order adaptive genetic algorithm in a cloud computing environment. Background technique [0002] Workflow under the cloud computing environment, referred to as "cloud workflow", is the integration of cloud computing and workflow-related technologies. Management, supply chain management and health care and other fields have broad application prospects. In cloud workflow, there are various types of computing resources and multiple tasks, and there are timing constraints between tasks. During execution, virtual machines are usually used as the smallest allocation unit of computing resources to receive and process these tasks. Cloud workflow execution or scheduling optimization refers to how to reasonably configur...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/48G06F9/50G06F9/455
CPCG06F9/4881G06F9/5072G06F9/45558G06F2009/4557G06F2209/483G06F2209/5021
Inventor 谢毅林荣雪
Owner ZHEJIANG GONGSHANG UNIVERSITY
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