TY - GEN
T1 - DAMCREM
T2 - 6th IEEE International Conference on Smart Computing, SMARTCOMP 2020
AU - Suzuki, Takuya
AU - Ishimaki, Yu
AU - Yamana, Hayato
N1 - Funding Information:
ACKNOWLEDGMENTS This work was supported by JST CREST Grant Number JPMJCR1503, Japan, and Japan-US Network Opportunity 2 by the Commissioned Research of National Institute of Information and Communications Technology, JAPAN. We are grateful to Assoc. Prof. Andrew Sohn of New Jersey Institute of Technology for his advice.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Smart computing aims to improve the quality of life by utilizing Internet-of-Things devices and cloud computing. Typically, this computing handles private and/or personal information so concealing such sensitive information is a challenge. Adopting fully homomorphic encryption (FHE) is one approach for handling such sensitive information safely; that is, we can calculate the encrypted data without decryption. However, the time and space complexity of the FHE operation is high. Thus, its computation takes a long time. In this study, we aim to shorten FHE execution time by adopting our new scheduling algorithm, which divides a task into several macro-tasks and then assigns a set of threads. We assume a cloud computing system that is equipped with a many-core CPU. Thus, we propose the dynamic allocation method of computation resource to macro-tasks (DAMCREM), which dynamically allocates a certain number of threads (selected from pre-defined candidates) to each macro-task of every given job. In the evaluation, we compared DAMCREM to naive methods that allocate a pre-defined number of threads to each macro-task. The result shows that the average latency and maximum latency of job execution is less than those of naive methods, even when the average interval of job arrival is short.
AB - Smart computing aims to improve the quality of life by utilizing Internet-of-Things devices and cloud computing. Typically, this computing handles private and/or personal information so concealing such sensitive information is a challenge. Adopting fully homomorphic encryption (FHE) is one approach for handling such sensitive information safely; that is, we can calculate the encrypted data without decryption. However, the time and space complexity of the FHE operation is high. Thus, its computation takes a long time. In this study, we aim to shorten FHE execution time by adopting our new scheduling algorithm, which divides a task into several macro-tasks and then assigns a set of threads. We assume a cloud computing system that is equipped with a many-core CPU. Thus, we propose the dynamic allocation method of computation resource to macro-tasks (DAMCREM), which dynamically allocates a certain number of threads (selected from pre-defined candidates) to each macro-task of every given job. In the evaluation, we compared DAMCREM to naive methods that allocate a pre-defined number of threads to each macro-task. The result shows that the average latency and maximum latency of job execution is less than those of naive methods, even when the average interval of job arrival is short.
KW - Client-server application
KW - Fully Homomorphic Encryption
KW - High performance computing
UR - http://www.scopus.com/inward/record.url?scp=85097349817&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097349817&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP50058.2020.00094
DO - 10.1109/SMARTCOMP50058.2020.00094
M3 - Conference contribution
AN - SCOPUS:85097349817
T3 - Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
SP - 458
EP - 463
BT - Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 September 2020 through 17 September 2020
ER -