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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö > Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö A

Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö A

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ¼­¹ö Ŭ·¯½ºÅÍ È¯°æ¿¡¼­ ¿¡³ÊÁö Àý¾àÀ» À§ÇÑ Çâ»óµÈ ¼­¹ö Àü·Â ¼Òºñ ÃßÁ¤ ¸ðµ¨
¿µ¹®Á¦¸ñ(English Title) An Improved Estimation Model of Server Power Consumption for Saving Energy in a Server Cluster Environment
ÀúÀÚ(Author) ±èµ¿ÁØ   °ûÈı٠  ±ÇÈñ¿õ   ±è¿µÁ¾   Á¤±Ô½Ä   Dongjun Kim   Hukeun Kwak   Huiung Kwon   Youngjong Kim   Kyusik Chung  
¿ø¹®¼ö·Ïó(Citation) VOL 19-A NO. 03 PP. 0139 ~ 0146 (2012. 06)
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(Korean Abstract)
¼­¹ö Ŭ·¯½ºÅÍ È¯°æ¿¡¼­ ¿¡³ÊÁö Àý¾àÀ» À§ÇÑ ¹æ¹ý Áß Çϳª´Â ¼­¹öÀÇ Àü¿øÀ» Æ®·¡ÇÈ »óȲ¿¡ ¸Â°Ô Á¦¾îÇÏ´Â Àü¿ø Á¦¾î ±â¼úÀÌ´Ù. ÀÌ´Â ÇöÀç µ¥ÀÌÅÍ ¼¾ÅÍÀÇ Àüü ¿¡³ÊÁö »ç¿ë·®°ú °¢ ¼­¹öÀÇ ¿¡³ÊÁö »ç¿ë·®À» ÆľÇÇÏ¿© ÀûÀýÇÏ°Ô ON/OFF »óÅ·Π°ü¸®ÇÏ´Â ±â¼úÀÌ´Ù. À̸¦ À§Çؼ­ °¢ ¼­¹öÀÇ Àü·ÂÀ» È¿°úÀûÀ¸·Î ÃßÁ¤ÇÏ´Â ¹æ½ÄÀÌ ÇÊ¿äÇѵ¥, º» ³í¹®¿¡¼­´Â ºñ¿ë ¸é°ú ¿¡³ÊÁö ¸é¿¡¼­ È¿À²ÀûÀÎ ¼ÒÇÁÆ®¿þ¾î ¹æ½ÄÀÇ ÃßÁ¤ ¸ðµ¨À» »ç¿ëÇÏ¿© Àü·ÂÀ» ÃßÁ¤ÇÑ´Ù. ¶ÇÇÑ ±âÁ¸ÀÇ Àü·Â ÃßÁ¤ ¸ðµ¨Àº CPUÀÇ À¯ÈÞ(idle) »ç¿ë·®¸¸À» »ç¿ëÇÔÀ¸·Î½á ÇöÀç ¼­¹öÀÇ ¼¼ºÎÀûÀÎ CPU »óųª I/O ÀåÄ¡ÀÇ »ç¿ë·®À» Á¤È®È÷ ÆľÇÇÏÁö ¸øÇÏ°í, ÀÌ´Â ÇØ´ç ¼­¹öÀÇ Àü·ÂÀ» È¿°úÀûÀ¸·Î ÃßÁ¤ÇÏÁö ¸øÇÏ´Â ´ÜÁ¡À¸·Î À̾îÁø´Ù. º» ³í¹®¿¡¼­´Â CPUÀÇ ´Ù¾çÇÑ »óÅ Çʵ带 È°¿ëÇÏ¿© ¼­¹öÀÇ CPU ¹× ½Ã½ºÅÛÀÇ Àü¹ÝÀûÀÎ »óŸ¦ º¸´Ù Á¤È®È÷ ÆľÇÇÏ°í, ÀÌ¿¡ µû¶ó ¼­¹öÀÇ Àü·ÂÀ» ±âÁ¸ÀÇ µÎ ¼ÒºñÀü·Â ÃßÁ¤ ¸ðµ¨(CPU/µð½ºÅ©/¸Þ¸ð¸® ±â¹ÝÀÇ Àü·Â ¼Òºñ ÃßÁ¤ ¸ðµ¨ ¹× CPU À¯ÈÞ°ª ±â¹ÝÀÇ Àü·Â ¼Òºñ ÃßÁ¤ ¸ðµ¨)º¸´Ù Á¤È®È÷ ÃøÁ¤ÇÏ´Â CPU Çʵå(field) ±â¹ÝÀÇ Àü·Â ÃßÁ¤ ¸ðµ¨À» Á¦¾ÈÇÑ´Ù. 2´ëÀÇ ¼­¹ö¸¦ »ç¿ëÇÏ¿© ½ÇÇèÀ» ¼öÇàÇÏ¿´À¸¸ç, Àü·Â°è¸¦ ÅëÇØ ÃøÁ¤ÇÑ ½ÇÁ¦ Àü·Â°ú °¢ ÃßÁ¤ ¸ðµ¨ÀÇ ÃßÁ¤ °ªÀ» ºñ±³ÇÏ¿© Æò±Õ ¿ÀÂ÷À²À» °è»êÇÏ¿´´Ù. ½ÇÇè °á°ú ±âÁ¸ ¼ÒºñÀü·Â ÃßÁ¤ ¸ðµ¨ÀÌ Æò±Õ 8-15%´ëÀÇ ¿ÀÂ÷À²À» º¸ÀÌ´Â ¹Ý¸é, º» ³í¹®¿¡¼­ Á¦¾ÈÇÏ´Â ¼­¹ö Àü·Â ÃßÁ¤ ¸ðµ¨Àº 2%´ëÀÇ ¿ÀÂ÷À²À» º¸¿© ÁÖ¾ú´Ù.
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(English Abstract)
In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.
Å°¿öµå(Keyword) ¼­¹ö Ŭ·¯½ºÅÍ   ¿¡³ÊÁö Àý°¨   ¼­¹ö ON/OFF   Àü·Â ÃßÁ¤ ¸ðµ¨   CPU Çʵ堠 Server Cluster   Saving Energy   Server On/Off   Power Estimation Model   CPU Fields  
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