Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
½Ç½Ã°£ ÀÀ¿ë¿¡¼ Ŭ¶ó¿ìµåÀÇ ½ºÄÉÁÙ¸µ Áö¿¬ ½Ã°£À» °í·ÁÇÑ ¿ÀÇÁ·Îµù °áÁ¤ ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
An Offloading Decision Scheme Considering the Scheduling Latency of the Cloud in Real-time Applications |
ÀúÀÚ(Author) |
¹Ú°æÈ
±èº´Èñ
±èÀº¼Ö
Á¶ÈÖ¿
À庴Ź
Kyung-Wha Park
Byoung-Hee Kim
Eun-Sol Kim
Hwi-Yeol Jo
Byoung-Tak Zhang
¹ÎÈ«
Á¤Áø¸¸
±èºÀÀç
ÇãÁØ¿µ
Hong Min
Jinman Jung
Bongjae Kim
Junyoung Heo
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¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 06 PP. 0392 ~ 0396 (2017. 06) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
Although mobile device-related technologies have developed rapidly, many problems arising from resource constraints have not been solved. Computation offloading that uses resources of cloud servers over the Internet was proposed to overcome physical limitations, and many studies have been conducted in terms of energy saving. However, completing tasks within their deadlines is more important than saving energy in real-time applications. In this paper, we proposed an offloading decision scheme considering the scheduling latency in the cloud to support real-time applications. The proposed scheme can improve the reliability of real-time tasks by comparing the estimated laxity of offloading a task with the estimated laxity of executing a task in a mobile device and selecting a more effective way to satisfy the task¡¯s deadline.
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Å°¿öµå(Keyword) |
ÀΰøÁö´É
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°¨¼º ÄÄÇ»ÆÃ
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artificial intelligence
machine learning
affective computing
lifelog
wearable sensors
automated tagging
ÄÄÇ»Å×ÀÌ¼Ç ¿ÀÇÁ·Îµù
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computation offloading
latency
real-time applications
cloud
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