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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ÇÐȸÁö > µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)

µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)

Current Result Document : 2 / 8 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ºòµ¥ÀÌÅÍÀÇ ¹Ýº¹ÀûÀÎ ¿¬»ê ÀÛ¾÷À» Áö¿øÇϱâ À§ÇÑ Hadoop ±â¹Ý ¼øȯó¸® ½Ã½ºÅÛ
¿µ¹®Á¦¸ñ(English Title) Hadoop-based Iterative Processing System for Repetitive Computations of Big data
ÀúÀÚ(Author) È«½ÂÅ   À±¹Î   ¹Ú°æ¼®   ÀÓä´ö   ÀåÀç¿ì   Seungtae Hong   n Yoon   Kyongseok Park   Chae Deok Lim   Jae-Woo Chang  
¿ø¹®¼ö·Ïó(Citation) VOL 32 NO. 01 PP. 0013 ~ 0030 (2016. 04)
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(Korean Abstract)
ÃÖ±Ù ºòµ¥ÀÌÅÍÀÇ È¿À²ÀûÀÎ ºÐ¼®À» À§ÇÏ¿©, ´ëÇ¥ÀûÀÎ MapReduce ÇÁ·¹ÀÓ¿öÅ©ÀÎ Hadoop¿¡ ´ëÇÑ ¿¬±¸°¡ È°¹ßÈ÷ ÀÌ·ç¾îÁö°í ÀÖ´Ù. ÇÑÆí, À¯Àüü µ¥ÀÌÅÍ ºÐ¼®°ú °°ÀÌ, ´ëºÎºÐÀÇ ºòµ¥ÀÌÅÍ ºÐ¼® ÀÀ¿ëÀº µ¿ÀÏÇÑ Map°ú Reduce ÇÔ¼öÀÇ ¹Ýº¹ÀûÀÎ ¼öÇàÀ» ¿ä±¸ÇÑ´Ù. ±×·¯³ª HadoopÀº ºñ¼øȯó¸® ±¸Á¶¸¦ °¡Áö°í Àֱ⠶§¹®¿¡, ¼øȯó¸® ÀÀ¿ë¿¡ ºñÈ¿À²ÀûÀÎ ¹®Á¦Á¡ÀÌ Á¸ÀçÇÑ´Ù. µû¶ó¼­ º» ³í¹®¿¡¼­´Â ºòµ¥ÀÌÅÍÀÇ ¹Ýº¹ÀûÀÎ ¿¬»ê ÀÛ¾÷À» Áö¿øÇϱâ À§ÇÑ Hadoop ±â¹Ý ¼øȯó¸® ½Ã½ºÅÛÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â ½Ã½ºÅÛÀº, ù°, ¹Ýº¹ÀûÀÎ MapReduce jobÀ» °ü¸®Çϱâ À§ÇØ ¼øȯó¸® job ½ºÄÉÁÙ¸µ ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. µÑ°, µ¥ÀÌÅÍ ÀÔÃâ·Â ºñ¿ëÀ» °¨¼Ò½ÃÅ°±â À§ÇØ ºÒº¯ µ¥ÀÌÅÍ Ä³½Ì ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. ¼Â°, ºÒÇÊ¿äÇÑ ¿¬»êÀ» ¹æÁöÇϱâ À§ÇØ Á¾·áÁ¶°Ç °Ë»ç ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. ³Ý°, HadoopŬ·¯½ºÅÍ ÀÚ¿øÀÇ È¿À²ÀûÀÎ °ü¸®¸¦ À§ÇØ ¼øȯó¸® ÀÚ¿ø °ü¸® ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. ¸¶Áö¸·À¸·Î, ±âÁ¸ ÇÏµÓ ±â¹Ý ½Ã½ºÅÛ°úÀÇ ºñ±³¸¦ ÅëÇØ Á¦¾ÈÇÏ´Â ½Ã½ºÅÛÀÇ ¼º´É ¿ì¼ö¼ºÀ» º¸ÀδÙ.
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(English Abstract)
Recently, in order to analyze big data efficiently, researches on Hadoop, one of the most popular MapReduce framework, have been actively done. Meanwhile, most of the big data analysis applications, e.g., genome data analysis, are required to do the same Map and Reduce functions repeatedly. However, Hadoop is inefficient for iterative data processing applications because it has a non-iterative processing structure. To solve this problem, we, in this paper, propose a Hadoop-based iterative processing system for supporting the repetitive computations of big data. In the proposed system, we first propose an iterative job scheduling technique for managing the iterative MapReduce jobs. Secondly, we propose an invariant data caching mechanism for reducing the I/O costs of data accesses. Thirdly, we propose a stopping condition check mechanism for preventing unnecessary computation. Fourthly, we propose an iterative resource scheduling technique for efficiently managing the resources of a Hadoop cluster. Finally, we show the performance superiority of the proposed system by comparing it with the existing Hadoop-based systems.
Å°¿öµå(Keyword) ºòµ¥ÀÌÅÍ   Hadoop   ¼øȯ󸮠  Ŭ¶ó¿ìµå ÄÄÇ»Æà  Big data   Hadoop   Iterative data processing   Cloud computing  
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