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Dynamic cloud workflow scheduling method based on genetic algorithm

A technology of genetic algorithm and scheduling method, which is applied in the field of cloud computing and intelligent algorithm, and can solve problems such as fixed

Inactive Publication Date: 2013-07-31
SUN YAT SEN UNIV
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Problems solved by technology

However, in the existing workflow scheduling model, the control flow topology of a workflow is fixed, that is, given by a single DAG
In practical applications, the control flow structure of cloud workflow may also have alternative branches such as IF-THEN, and its control flow topology structure also has the characteristics of dynamic and time-varying. How can cloud environment and cloud work be considered in the scheduling process of cloud workflow? The dynamic and time-varying characteristics of the flow control topology, thereby further improving the availability of the cloud workflow scheduling system in a dynamic and time-varying environment, poses new challenges to the cloud workflow scheduling method

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  • Dynamic cloud workflow scheduling method based on genetic algorithm
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  • Dynamic cloud workflow scheduling method based on genetic algorithm

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[0024] The method of the invention will be further described below in conjunction with the accompanying drawings.

[0025] Cloud workflow can be expressed by a directed acyclic graph G=(V, A), where the set of nodes V={T 1 ,T 2 ,...,T n} corresponds to the computing tasks in the workflow, n is the number of tasks contained in the workflow, and a directed edge (T i ,T j ) means task T i and T j The priority constraint relationship between tasks T j Only in its parent task T i Execution cannot begin until complete. In the cloud computing environment, each task can be realized through a variety of different cloud computing services, that is, the task T i Corresponds to a series of cloud services related to it by Indicates a method that can be used to realize T i cloud computing services, m i is task T i The corresponding total number of all available cloud services. The attributes of a cloud service can be represented by a set of 2-tuples in, and respectivel...

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Abstract

The invention discloses a method for optimizing a dynamic cloud workflow by using a genetic algorithm. The algorithm aims at minimizing periodic average expense of the cloud workflow in iteration execution, and limiting the total execution time that the workflow is executed for on period in a dynamic environment each time within a maximum execution deadline defined by a user. As the execution mode of the workflow is dynamic and variable in the cloud realization environment, the method carries out overall modeling on all flow topology results which may occur, then the characteristics of dynamic time-variation of the cloud workflow are comprehensively considered, the average performance of the execution of the cloud workflow in the dynamic environment is optimized by using the genetic algorithm, and the execution efficiency of the cloud workflow is improved.

Description

Technical field: [0001] The invention relates to two fields of cloud computing and intelligent algorithms, and mainly relates to a dynamic cloud workflow scheduling method based on genetic algorithms. technical background: [0002] Cloud computing is a new type of computing model that has emerged rapidly in recent years, and has become an important development direction during the 12th Five-Year Plan period in my country. Cloud computing realizes on-demand provision of various computing services to users through the virtual aggregation and sharing of a large number of computing resources, so it can meet the growing demand for big data processing. In order to further improve the cloud computing system's ability to manage and process big data, how to reasonably and efficiently schedule cloud computing resources to provide users with elastic computing services is the key to improving the performance of cloud computing systems. [0003] In the cloud computing environment, due t...

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Application Information

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IPC IPC(8): G06Q10/06G06N3/12
Inventor 张军陈伟能尹亮
Owner SUN YAT SEN UNIV
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