Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
ÇÏÀ̺긮µå Ŭ¶ó¿ìµå ȯ°æ¿¡¼ÀÇ ÀÀ¿ë Ư¼º °¡ÁßÄ¡¸¦ °í·ÁÇÑ ÀÚ¿ø ±ºÁýÈ ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
A Resource Clustering Method Considering Weight of Application Characteristic in Hybrid Cloud Environment |
ÀúÀÚ(Author) |
¿ÀÀ¯¸®
±èÀ±Èñ
Yoori Oh
Yoonhee Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 08 PP. 0481 ~ 0486 (2017. 08) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
There are many scientists who want to perform experiments in a cloud environment, and pay-per-use services allow scientists to pay only for cloud services that they need. However, it is difficult for scientists to select a suitable set of resources since those resources are comprised of various characteristics. Therefore, classification is needed to support the effective utilization of cloud resources. Thus, a dynamic resource clustering method is needed to reflect the characteristics of the application that scientists want to execute. This paper proposes a resource clustering analysis method that takes into account the characteristics of an application in a hybrid cloud environment. The resource clustering analysis applies a Self-Organizing Map and K-means algorithm to dynamically cluster similar resources. The results of the experiment indicate that the proposed method can classify a similar resource cluster by reflecting the application characteristics.
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Å°¿öµå(Keyword) |
ÇÏÀ̺긮µå Ŭ¶ó¿ìµå
ÀÚ±âÁ¶Á÷ÈÁöµµ
ÀÚ¿ø ºÐ·ù
ÀÀ¿ë Ư¼º °¡ÁßÄ¡
hybrid cloud
self-organizing map
resource classification
application characteristic weight
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