TY - GEN
T1 - Glaucus
T2 - 9th International Conference on Intelligent Computing, ICIC 2013
AU - Liu, Xia
AU - Zhou, Zhigang
AU - Du, Xiaojiang
AU - Zhang, Hongli
AU - Wu, Junchao
PY - 2013
Y1 - 2013
N2 - As Cloud computing has gained much popularity recently, many organizations consider transmitting their large-scale computing-intensive programs to cloud. However, cloud service market is still in its infant stage. Many companies offer a variety of cloud computing services with different pricing schemes, while customers have the demand of "spending the least, gaining the most". It makes a challenge which cloud service provider is more suitable for their programs and how much computing resource should be purchased. To address this issue, in this paper, we present a performance prediction scheme for computing-intensive program on cloud. The basic idea is to map program into an abstract tree, and create a miniature version program, and insert checkpoints in head and tail for each computable independent unit, which record the beginning & end timestamp. Then we use the method of dynamic analysis, run the miniature version program on small data locally, and predict the whole program's cost on cloud. We find several features which have close relationship with program's performance, and through analyzing these features we can predict program's cost on the cloud. Our real-network experiments show that the scheme can achieve high prediction accuracy with low overhead.
AB - As Cloud computing has gained much popularity recently, many organizations consider transmitting their large-scale computing-intensive programs to cloud. However, cloud service market is still in its infant stage. Many companies offer a variety of cloud computing services with different pricing schemes, while customers have the demand of "spending the least, gaining the most". It makes a challenge which cloud service provider is more suitable for their programs and how much computing resource should be purchased. To address this issue, in this paper, we present a performance prediction scheme for computing-intensive program on cloud. The basic idea is to map program into an abstract tree, and create a miniature version program, and insert checkpoints in head and tail for each computable independent unit, which record the beginning & end timestamp. Then we use the method of dynamic analysis, run the miniature version program on small data locally, and predict the whole program's cost on cloud. We find several features which have close relationship with program's performance, and through analyzing these features we can predict program's cost on the cloud. Our real-network experiments show that the scheme can achieve high prediction accuracy with low overhead.
KW - Cloud computing
KW - cost
KW - performance
KW - predict
UR - http://www.scopus.com/inward/record.url?scp=84882761402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882761402&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39479-9_34
DO - 10.1007/978-3-642-39479-9_34
M3 - Conference contribution
AN - SCOPUS:84882761402
SN - 9783642394782
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 285
EP - 294
BT - Intelligent Computing Theories - 9th International Conference, ICIC 2013, Proceedings
Y2 - 28 July 2013 through 31 July 2013
ER -