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Workflow execution optimization method based on multi-population genetic algorithm in cloud computing environment

A cloud computing environment, genetic algorithm technology, applied in cloud workflow execution optimization, workflow execution optimization field based on multi-swarm genetic algorithm, can solve the lack of resource allocation and task scheduling integrated collaborative optimization method, cloud workflow execution performance low level issues

Pending Publication Date: 2020-04-17
ZHEJIANG UNIV OF TECH
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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 execution optimization method based on multi-population genetic algorithm in the environment effectively improves the performance of cloud workflow execution

Method used

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  • Workflow execution optimization method based on multi-population genetic algorithm in cloud computing environment
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  • Workflow execution optimization method based on multi-population genetic algorithm in cloud computing environment

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Embodiment Construction

[0151] 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.

[0152] 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.

[0153]

[0154] Table 1

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[0157] Table 2

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Abstract

The invention discloses a workflow execution optimization method based on a multi-population genetic algorithm in a cloud computing environment. The workflow execution optimization method comprises the following steps: acquiring information required by execution optimization; calculating a hierarchical value of the task; initializing a contemporary population; decoding the improved contemporary population and calculating a fitness value; performing independent evolution on a plurality of sub-populations, and timely performing communication among the sub-populations; and outputting an executionoptimization result until a termination condition is satisfied. Compared with a traditional method, the methods and strategies of individual random generation based on the hierarchy and the efficiency ratio, integer coding based on topological sorting, serial individual decoding based on the insertion mode, forward and backward individual decoding improvement, multi-population coordinated evolution and the like are designed and adopted; integrated collaborative optimization of resource allocation and task scheduling is realized, the optimization capability is improved, and the overall efficiency of the algorithm is improved.

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 execution optimization method, and more specifically, to a workflow execution optimization method based on a multi-population 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 reaso...

Claims

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Application Information

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IPC IPC(8): G06F9/48G06F9/50G06F9/455G06N3/12
CPCG06F9/4881G06F9/485G06F9/5072G06F9/5038G06F9/45558G06N3/126G06F2009/4557Y02D10/00
Inventor 单晓杭
Owner ZHEJIANG UNIV OF TECH
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