Open Access

ARTICLE

Data Layout and Scheduling Tasks in a Meteorological Cloud Environment

Kunfu Wang, Yongsheng Hao, Jie Cao^*

School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Jie Cao. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 1033-1052. https://doi.org/10.32604/iasc.2023.038036

Received 24 November 2022; Accepted 15 February 2023; Issue published 29 April 2023

Abstract

Meteorological model tasks require considerable meteorological basis data to support their execution. However, if the task and the meteorological datasets are located on different clouds, that enhances the cost, execution time, and energy consumption of execution meteorological tasks. Therefore, the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations. To the best of our knowledge, this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout. First, we use the FP-Growth-M (frequent-pattern growth for meteorological model datasets) method to mine the relationship between meteorological models and datasets. Second, based on the relation, we propose a heuristics algorithm for laying out the meteorological datasets and scheduling tasks. Finally, we use simulation results to compare our proposed method with other methods. The simulation results show that our method reduces the number of involved clouds, the sizes of files from outer clouds, and the time of transmitting files.

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Keywords

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