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Enhanced Best Fit Algorithm for Merging Small Files

Computer Systems Science & Engineering 2023年第7期46卷 913-928页

作者： Adnan Ali nada masood mirza Mohamad Khairi Ishak School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong TebalPulau Pinang14300Malaysia University College United Arab Emirates UniversityAl AinUAE

In the Big Data era,numerous sources and environments generate massive amounts of *** enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future *** is used to process this kind of *** is known to handle vast volumes of data more efficiently than tiny amounts,which results in inefficiency in the *** study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm(EBFM)that merges files depending on predefined parameters(type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same *** primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established *** procedure takes place before the files are available for the Hadoop ***,the files generated by the system are named with specific keywords to ensure there is no data loss(file overwrite).The proposed approach guarantees the generation of the fewest possible large files,which reduces the input/output memory burden and corresponds to the Hadoop framework’s *** findings show that the proposed technique enhances the framework’s performance by approximately 64%while comparing all other potential performance-impairing *** proposed approach is implementable in any environment that uses the Hadoop framework,not limited to smart cities,real-time data analysis,etc.

关键词： Big data Hadoop MapReduce small file HDFS

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