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Scientific workflow task scheduling algorithm for improving binary particle swarm optimization

A particle swarm algorithm and task scheduling technology, which is applied in the field of scientific workflow and can solve problems such as poor convergence of binary particle swarm algorithm

Pending Publication Date: 2022-03-04
TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0004] How to solve the problem of task allocation in the cloud environment for applications such as scientific workflow so as to effectively reduce energy consumption and improve resource utilization, an improved binary particle swarm algorithm is proposed, and the update formula of particles is modified to solve the problem of The problem of poor convergence of the original binary particle swarm optimization algorithm

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  • Scientific workflow task scheduling algorithm for improving binary particle swarm optimization
  • Scientific workflow task scheduling algorithm for improving binary particle swarm optimization
  • Scientific workflow task scheduling algorithm for improving binary particle swarm optimization

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Embodiment Construction

[0019] The present invention is based on the scientific workflow task scheduling algorithm method of the improved binary particle swarm algorithm, and the method comprises the following steps:

[0020] Step 1: Generate the initial population. The quality of the initial population is related to the speed of the search. A good initial population is conducive to the algorithm to quickly find the optimal solution. A group of particles is generated by computer random algorithm as the initial population. To generate the initial population, each dimension of each particle is randomly determined to be "0" or "1". A random number between [0, 1] is generated by the computer. This random number can be guaranteed to be fully random, and its probability in the interval [0, 0.5] and [0.5, 1] ​​is both 0.5. Determine whether the value of this dimension is "0" or "1" by judging which interval the random number is in. The corresponding calculation formula is as follows:

[0021]

[0022]...

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Abstract

The invention relates to the technical field of data-intensive scientific workflow, in particular to a scientific workflow task scheduling algorithm based on an improved binary particle swarm algorithm. The method has the main technical characteristics that the binary particle swarm optimization algorithm is improved, the detection capability of an optimal solution is improved, the obtained optimal solution has better actual scheduling efficiency and cost, and the resource utilization rate is improved. According to an optimization mode of a particle swarm algorithm, in the flying process of particles, the particles get closer to the optimal particles, the bit change probability should be smaller, the binary particle swarm algorithm is larger, and the search ability of the algorithm is influenced. The proposed algorithm is adjusted based on an optimization mode of the particle swarm optimization algorithm, the particle speed is used as a correction item of the particle position, in the improvement of the binary particle swarm optimization algorithm, a speed iterative formula is still kept unchanged, and a speed normalization formula sigm function and a position iterative formula are trimmed.

Description

technical field [0001] The invention relates to the technical field of scientific workflow, in particular to a scientific workflow task scheduling algorithm based on an improved binary particle swarm algorithm. Background technique [0002] With the advent of the era of big data, scientific workflows have gradually attracted people's attention. A scientific workflow is a class of computational tasks that can be fully or partially automated to Figure 1 It is expressed in the form of a directed acyclic graph DAG graph, and there are certain data dependencies and timing relationships between tasks. With the development of cloud computing technology, the application fields of cloud computing are becoming wider and wider. Many big data applications in scientific research and business fields rely on the cloud environment for execution. In the face of large amount of application data and high consumption of computing resources, how to improve resource scheduling efficiency and re...

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Application Information

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IPC IPC(8): G06N3/00G06F30/27
CPCG06N3/006G06F30/27
Inventor 熊聪聪高萌赵青
Owner TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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