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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ³í¹®Áö C : ÄÄÇ»ÆÃÀÇ ½ÇÁ¦

Á¤º¸°úÇÐȸ ³í¹®Áö C : ÄÄÇ»ÆÃÀÇ ½ÇÁ¦

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) MapReduce ÇÁ·¹ÀÓ¿öÅ©ÀÇ ÀÛ¾÷ ¿Ï·á ½Ã°£ ´ÜÃàÀ» À§ÇÑ JobTracker °áÇÔ Çã¿ë ¸ÞÄ¿´ÏÁò
¿µ¹®Á¦¸ñ(English Title) A JobTracker Fault-tolerant Mechanism for Reducing Job Completion Time in the MapReduce Framework
ÀúÀÚ(Author) °­¹Î±¸   ¹Ú±âÁø   Ȳº´Çö   Minkoo Kang   Kiejin Park   Byeonghyeon Hwang  
¿ø¹®¼ö·Ïó(Citation) VOL 18 NO. 03 PP. 0173 ~ 0180 (2012. 03)
Çѱ۳»¿ë
(Korean Abstract)
Ŭ¶ó¿ìµå ÄÄÇ»Æà ¼­ºñ½º¸¦ Á¦°øÇϱâ À§Çؼ­´Â µ¥ÀÌÅÍÀÇ ºÐ»ê ÀúÀå ¹× º´·Ä 󸮰¡ °¡´ÉÇÑ IT ÀÎÇÁ¶ó ±¸ÃàÀÌ ÇʼöÀûÀÌ´Ù. À̸¦ À§Çؼ­ ´ë¿ë·® µ¥ÀÌÅÍÀÇ ºÐ»ê 󸮰¡ Áö¿øµÇ´Â MapReduce ÇÁ·¹ÀÓ¿öÅ©°¡ °¢±¤ ¹Þ°í ÀÖ´Ù. MapReduce ÇÁ·¹ÀÓ¿öÅ©´Â Àúºñ¿ëÀ¸·Î ºÐ»ê º´·Ä ó¸® ½Ã½ºÅÛÀ» ±¸ÃàÇϱ⿡ È¿°úÀûÀÌÁö¸¸, Àüü MapReduce ÀÛ¾÷ÀÇ ½ºÄÉÁÙ¸µ ¹× ÀÛ¾÷ ÇÒ´çÀ» ´ã´çÇÏ´Â JobTracker°¡ SPOF(Single Point of Failure)¶ó´Â ¹®Á¦°¡ ÀÖ´Ù. ÀÌ·Î ÀÎÇØ MapReduce ÀÛ¾÷ µµÁß JobTracker¿¡ °áÇÔÀÌ ¹ß»ýÇÏ°Ô µÇ¸é Àüü MapReduce ÀÛ¾÷À» óÀ½ºÎÅÍ ´Ù½Ã ½ÃÀÛÇØ¾ß ÇϹǷΠÀÛ¾÷ ¿Ï·á ½Ã°£ÀÌ Áõ°¡ÇÑ´Ù. À§¿Í °°Àº ¹®Á¦¸¦ ÇØ°áÇÏ°íÀÚ º» ³í¹®¿¡¼­´Â MapReduce ÇÁ·¹ÀÓ¿öÅ©ÀÇ JobTracker °áÇÔ Çã¿ë ¸ÞÄ¿´ÏÁòÀ» ¼³°è・±¸ÇöÇÏ°í, MapReduce Å×½ºÆ®º£µå¿Í °áÇÔ ÁÖÀÔ ±â¹ýÀ» ÀÌ¿ëÇÏ¿© ¼º´É Æò°¡¸¦ ½Ç½ÃÇÏ¿´´Ù. ±× °á°ú, ±âÁ¸ MapReduce¿¡ ºñÇØ JobTracker °áÇÔ Çã¿ëÀÌ Àû¿ëµÈ MapReduceÀÇ Æò±Õ ÀÛ¾÷ ¿Ï·á ½Ã°£Àº 46.5%¢¦64.4% °¨¼ÒµÊÀ» È®ÀÎÇÏ¿´´Ù.
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(English Abstract)
In order to effectively provide cloud computing, IT infrastructure which supports distributed file system and parallel data processing is essential. To this end, MapReduce framework has been widely used for distributed processing of large-scale data. MapReduce framework has been proven as an efficient way to construct distributed and parallel processing system at relatively low cost. However, it has the problem of single point of failure (SPOF) at JobTracker that is responsible for scheduling and assigning of all MapReduce tasks. When JobTracker has failed, the completion time of the MapReduce job is increased because the entire MapReduce tasks must be restarted. To resolve the above mentioned problem we designed and implemented JobTracker fault-tolerant mechanism for MapReduce framework. The performance of the mechanism is evaluated by using MapReduce testbed and fault-injection method. As a result, the average job completion time of the mechanism is dramatically reduced about 46.5%¢¦64.4% compared to the result of a naive MapReduce.
Å°¿öµå(Keyword) Ŭ¶ó¿ìµå ÄÄÇ»Æà  °áÇÔ Çã¿ë   Cloud Computing   MapReduce   JobTracker   Fault-tolerant  
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