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MapReduce Parallel Programming Model: A State-of-the-Art Survey.

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Τίτλος: MapReduce Parallel Programming Model: A State-of-the-Art Survey.
Συγγραφείς: Li, Ren1 renli@cqjtu.edu.cn, Hu, Haibo2 hbhu@cqu.edu.cn, Li, Heng2 liheng@cqu.edu.cn, Wu, Yunsong3 yswu@cqu.edu.cn, Yang, Jianxi1 yjx@cquc.edu.cn
Πηγή: International Journal of Parallel Programming. Aug2016, Vol. 44 Issue 4, p832-866. 35p.
Θεματικοί όροι: *Big data, *Open source software, *Scalability, Parallel programs (Computer programs), Cloud computing
Περίληψη: With the development of information technologies, we have entered the era of Big Data. Google's MapReduce programming model and its open-source implementation in Apache Hadoop have become the dominant model for data-intensive processing because of its simplicity, scalability, and fault tolerance. However, several inherent limitations, such as lack of efficient scheduling and iteration computing mechanisms, seriously affect the efficiency and flexibility of MapReduce. To date, various approaches have been proposed to extend MapReduce model and improve runtime efficiency for different scenarios. In this review, we assess MapReduce to help researchers better understand these novel optimizations that have been taken to address its limitations. We first present the basic idea underlying MapReduce paradigm and describe several widely used open-source runtime systems. And then we discuss the main shortcomings of original MapReduce. We also review these MapReduce optimization approaches that have recently been put forward, and categorize them according to the characteristics and capabilities. Finally, we conclude the paper and suggest several research works that should be carried out in the future. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Parallel Programming is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1007/s10766-015-0395-0
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Parallel+Programming%22">International Journal of Parallel Programming</searchLink>. Aug2016, Vol. 44 Issue 4, p832-866. 35p.
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  Data: With the development of information technologies, we have entered the era of Big Data. Google's MapReduce programming model and its open-source implementation in Apache Hadoop have become the dominant model for data-intensive processing because of its simplicity, scalability, and fault tolerance. However, several inherent limitations, such as lack of efficient scheduling and iteration computing mechanisms, seriously affect the efficiency and flexibility of MapReduce. To date, various approaches have been proposed to extend MapReduce model and improve runtime efficiency for different scenarios. In this review, we assess MapReduce to help researchers better understand these novel optimizations that have been taken to address its limitations. We first present the basic idea underlying MapReduce paradigm and describe several widely used open-source runtime systems. And then we discuss the main shortcomings of original MapReduce. We also review these MapReduce optimization approaches that have recently been put forward, and categorize them according to the characteristics and capabilities. Finally, we conclude the paper and suggest several research works that should be carried out in the future. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Parallel Programming is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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