TY - GEN
T1 - Implementations of Online Job Acceptance Functions in User-PC Computing System
AU - Htet, Hein
AU - Funabiki, Nobuo
AU - Kamoyedji, Ariel
AU - Zhou, Xudong
AU - Watequlis Syaifudin, Yan
AU - Tri Anggraini, Irin
AU - Kuribayashi, Minoru
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As a low-cost and high-performance distributed computing platform, user-PC computing (UPC) system has been studied based on the master-worker model. It uses idling resources of personal computers (PCs) of users for workers. Docker containers are introduced to run the jobs of applications programs on various PC environments. Previously, the UPC system only accepts jobs from users manually using the web interface. In this paper, we implement two online job acceptance functions in the UPC system using Secure Shell File Transfer Protocol (SFTP) and a cloud storage, so that application systems can submit jobs and receive results online. For evaluations, we adopt Android programming learning assistance system (APLAS) and Exercise and performance learning assistant system (EPLAS) as the application systems, which have been developed in our group, and pCloud for the cloud storage. The experiment results show that the total CPU time is reduced by 90.5% for APLAS and 55.1% for EPLAS of the original, respectively.
AB - As a low-cost and high-performance distributed computing platform, user-PC computing (UPC) system has been studied based on the master-worker model. It uses idling resources of personal computers (PCs) of users for workers. Docker containers are introduced to run the jobs of applications programs on various PC environments. Previously, the UPC system only accepts jobs from users manually using the web interface. In this paper, we implement two online job acceptance functions in the UPC system using Secure Shell File Transfer Protocol (SFTP) and a cloud storage, so that application systems can submit jobs and receive results online. For evaluations, we adopt Android programming learning assistance system (APLAS) and Exercise and performance learning assistant system (EPLAS) as the application systems, which have been developed in our group, and pCloud for the cloud storage. The experiment results show that the total CPU time is reduced by 90.5% for APLAS and 55.1% for EPLAS of the original, respectively.
KW - APLAS
KW - Docker
KW - EPLAS
KW - online job acceptance
KW - pCloud
KW - SFTP
KW - UPC system
UR - http://www.scopus.com/inward/record.url?scp=85129190052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129190052&partnerID=8YFLogxK
U2 - 10.1109/LifeTech53646.2022.9754807
DO - 10.1109/LifeTech53646.2022.9754807
M3 - Conference contribution
AN - SCOPUS:85129190052
T3 - LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
SP - 121
EP - 122
BT - LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Y2 - 7 March 2022 through 9 March 2022
ER -