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ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
A Distributed Real-time Self-Diagnosis System for Processing Large Amounts of Log Data |
ÀúÀÚ(Author) |
¼Õ½Ã¿î
±è´Ù¼Ö
¹®¾ç¼¼
ÃÖÇüÁø
Siwoon Son
Dasol Kim
Yang-Sae Moon
Hyung-Jin Choi
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¿ø¹®¼ö·Ïó(Citation) |
VOL 34 NO. 03 PP. 0058 ~ 0068 (2018. 12) |
Çѱ۳»¿ë (Korean Abstract) |
ºÐ»ê ÄÄÇ»ÆÃÀ̶õ ´Ù¼öÀÇ ¼¹ö·Î ±¸¼ºµÈ ºÐ»ê ½Ã½ºÅÛ¿¡¼ µ¥ÀÌÅ͸¦ È¿À²ÀûÀ¸·Î ÀúÀå ¹× Ã³¸®ÇÏ´Â ±â¼úÀÌ´Ù. µû¶ó¼ ºÐ»ê ½Ã½ºÅÛÀ» ±¸¼ºÇÏ´Â ¼¹öÀÇ »óÅ¿¡ µû¶ó ºÐ»ê ÄÄÇ»ÆÃÀÇ ¼º´É¿¡ Å« ¿µÇâÀ» ¹ÌÄ£´Ù. º» ³í¹®Àº ºÐ»ê ½Ã½ºÅÛ¿¡¼ ½Ç½Ã°£À¸·Î ¹ß»ýÇÏ´Â ½Ã½ºÅÛ ÀÚ¿øÀÇ ·Î±× µ¥ÀÌÅ͸¦ ¼öÁýÇÏ°í ÀÌ»óÀ» ŽÁöÇÏ¿© °á°ú¸¦ ½Ã°¢ÈÇÏ´Â ÀÚ°¡ Áø´Ü ½Ã½ºÅÛÀ» Á¦¾ÈÇÑ´Ù. ¸ÕÀú, ÀÚ°¡ Áø´Ü °úÁ¤À» ¼öÁý, Àü´Þ, ºÐ¼®, ÀúÀå, ½Ã°¢ÈÀÇ ´Ù¼¸ ´Ü°è·Î ±¸ºÐÇÑ´Ù. ´ÙÀ½À¸·Î, ÀÚ°¡ Áø´Ü °úÁ¤ÀÌ ½Ç½Ã°£¼º, È®À强, °í°¡¿ë¼ºÀÇ ¸ñÇ¥¸¦ ¸¸Á·Çϵµ·Ï ½Ç½Ã°£ ÀÚ°¡ Áø´Ü ½Ã½ºÅÛÀ» ¼³°èÇÑ´Ù. º» ½Ã½ºÅÛÀº ´ëÇ¥ÀûÀÎ ½Ç½Ã°£ ºÐ»ê ±â¼úÀÎ Apache Flume, Apache Kafka, Apache StormÀ» ±â¹ÝÀ¸·Î ±¸ÇöµÇ¾î ½Ç½Ã°£¼º, È®À强, °í°¡¿ë¼ºÀÇ ¼¼ °¡Áö ¸ñÇ¥¸¦ ¸¸Á·ÇÒ ¼ö ÀÖ´Ù. ¶ÇÇÑ, ÀÚ°¡ Áø´Ü °úÁ¤¿¡¼ ·Î±× µ¥ÀÌÅÍ Ã³¸®ÀÇ Áö¿¬À» ÃÖ¼ÒÈÇϵµ·Ï °£´ÜÇÏÁö¸¸ È¿°úÀûÀÎ À̵¿ Æò±Õ ¹× 3-½Ã±×¸¶ ±â¹Ý ÀÌ»ó ŽÁö ±â¹ýÀ» »ç¿ëÇÑ´Ù. º» ³í¹®ÀÇ °á°ú¸¦ ÅëÇØ, ºÐ»ê ½Ã½ºÅÛ ³»¿¡¼ ¼¹ö »óŸ¦ ½Ç½Ã°£À¸·Î Áø´ÜÇÒ ¼ö ÀÖ´Â ºÐ»ê ½Ç½Ã°£ ÀÚ°¡ Áø´Ü ½Ã½ºÅÛÀ» ±¸ÃàÇÒ ¼ö ÀÖ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.
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Å°¿öµå(Keyword) |
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½Ç½Ã°£ ó¸®
ÀÚ°¡ Áø´Ü
µ¥ÀÌÅÍ ½ºÆ®¸²
Big data
Distributed computing
Real-time processing
Self-diagnosis
Data stream
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