Minimal MapReduce algorithms
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of tera-bytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and network transfer at each machine. Although these principles have guided the development of MapReduce algorithms, limited emphasis has been placed on enforcing serious constraints on the aforementioned metrics simultaneously. This paper presents the notion of minimal algorithm, that is, an algorithm that guarantees the best parallelization in multiple aspects at the same time, up to a small constant factor. We show the existence of elegant minimal algorithms for a set of fundamental database problems, and demonstrate their excellent performance with extensive experiments. Copyright © 2013 ACM.
著者Tao Y., Lin W., Xiao X.
會議名稱2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013
會議開始日22.06.2013
會議完結日27.06.2013
會議地點New York, NY
會議國家/地區美國
詳細描述organized by ACM,
出版年份2013
月份7
日期29
頁次529 - 540
國際標準書號9781450320375
國際標準期刊號0730-8078
語言英式英語
關鍵詞Big data, MapReduce, Minimal algorithm

上次更新時間 2020-14-10 於 01:58