Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ ³í¹®Áö
ÇѱÛÁ¦¸ñ(Korean Title) |
Ŭ¶ó¿ìµå µ¥ÀÌÅÍ ¼¾ÅÍ¿¡¼ °¡»óÈµÈ ÀÚ¿øÀÇ SLA-Aware Á¶Á¤À» ÅëÇÑ ¼º´É ¹× ¿¡³ÊÁö È¿À²ÀÇ ÃÖÀûÈ |
¿µ¹®Á¦¸ñ(English Title) |
Optimizing Performance and Energy Efficiency in Cloud Data Centers Through SLA-Aware Consolidation of Virtualized Resources |
ÀúÀÚ(Author) |
ÇÁ·©Å© ¿¤¸®È£µ¥
ÀÌÀç¿Ï
Frank I. Elijorde
Jaewan Lee
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 15 NO. 03 PP. 0001 ~ 0010 (2014. 06) |
Çѱ۳»¿ë (Korean Abstract) |
Ŭ¶ó¿ìµå ÄÄÇ»ÆÃÀº »ç¿ëÀÚÀÇ ¿ä±¸¿¡ µû¶ó IT¼ºñ½º°¡ »ý¼º ¹× Á¶Á¤µÇ´Â pay-per use ¸ðµ¨À» µµÀÔÇÏ¿´´Ù. ±×·¯³ª ¼ºñ½º Á¦°øÀÚ´Â ¾ÆÁ÷µµ ¹°¸®ÀûÀÎ ÀÎÇÁ¶ó·Î ÀÎÇØ ¹ß»ýÇÏ´Â Á¦¾àÁ¶°Çµé¿¡ ´ëÇØ °ü½ÉÀ» °®°í ÀÖ´Ù. ÇÊ¿äÇÑ QoS³ª SLA¸¦ ¸¸Á·½ÃÅ°±â À§Çؼ´Â °¡»óÈµÈ ÀÚ·áµéÀÌ ¿¡³ÊÁö ¼Òºñ·®À» ÃÖ¼ÒȽÃÅ°¸é¼ ½Ã½ºÅÛ ¼º´ÉÀ» ÃÖ´ëȽÃÅ°±â À§ÇØ Á¶Á¤µÇ¾î¾ß ÇÑ´Ù. º» ¿¬±¸´Â ANNÀ» »ç¿ëÇÏ¿© Ŭ¶ó¿ìµå ȯ°æ¿¡¼ °¡»óÈµÈ ÀÚ¿øµéÀ» Á¶Á¤Çϱâ À§ÇÑ ¿¹ÃøÀû SLA ¾î¿þ¾î ¹æ¾ÈÀ» Á¦½ÃÇÑ´Ù. Qos¸¦ À¯ÁöÇÏ°í, ¼º´É°ú ¿¡³ÊÁö È¿À²°£ÀÇ ÃÖÀûȸ¦ À§Çؼ ¼¹ö È°¿ë ÀÓ°èÄ¡´Â ¹°¸®Àû ÀÚ¿øÀÇ ¼Òºñ¿¡ µû¶ó µ¿ÀûÀ¸·Î Àû¿ëÇÑ´Ù. ¶ÇÇÑ ¸¹Àº ÀÚ¿øÀ» ¼ÒºñÇÏ´Â VMµéÀº ´É·ÂÀÖ°í ÆòÆÇÀÌ ÁÁÀº È£½ºÆ®¿¡ ÇÒ´çÇÔÀ¸·Î½á ºÎÁ·ÇÑ ÇÁ·ÎºñÀü´×À» ¹æÁöÇÑ´Ù. Á¦¾ÈÇÑ ±â¹ýÀÇ ¼º´ÉÀ» Æò°¡Çϱâ À§ÇØ, ÀÌÁúÀûÀΠŬ¶ó¿ìµå ȯ°æ¿¡¼ ÃÖÀûȵÇÁö ¾ÊÀº ÀüÅëÀûÀÎ Á¢±Ù¹æ¹ý ¹× ±âÁ¸ÀÇ ±â¹ýµé°ú ºñ±³ÇÏ¿´´Ù.
|
¿µ¹®³»¿ë (English Abstract) |
The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server¡¯s utilization threshold dynamically adapts to the physical machine¡¯s resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.
|
Å°¿öµå(Keyword) |
Ŭ¶ó¿ìµå ÄÄÇ»ÆÃ
Ŭ¶ó¿ìµå µ¥ÀÌÅͼ¾ÅÍ
Àΰø½Å°æ¸Á
ÀÚ¿ø ÇÁ·ÎºñÀú´×
±×¸° ÄÄÇ»ÆÃ
Cloud Computing
Cloud Data Centers
Artificial Neural Network
Resource Provisioning
Green Computing
|
ÆÄÀÏ÷ºÎ |
PDF ´Ù¿î·Îµå
|