Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological datasets are located on different clouds,that enhances the cost,executio...Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological 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.展开更多
基金funded in part byMajor projects of the National Social Science Fund(16ZDA054)of Chinathe Postgraduate Research&Practice Innovation Program of Jiansu Province(NO.KYCX18_0999)of Chinathe Engineering Research Center for Software Testing and Evaluation of Fujian Province(ST2018004)of China.
文摘Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological 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.
