TY - GEN
T1 - When good enough is better
T2 - 31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
AU - Hui, Xinning
AU - Du, Zhihui
AU - Liu, Jason
AU - Sun, Hongyang
AU - He, Yuxiong
AU - Bader, David A.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - Power is a primary concern for mobile, cloud, and high-performance computing applications. Approximate computing refers to running applications to obtain results with tolerable errors under resource constraints, and it can be applied to balance energy consumption with service quality. In this paper, we propose a 'Good Enough (GE)' scheduling algorithm that uses approximate computing to provide satis- factory QoS (Quality of Service) for interactive applications with significant energy savings. Given a user-specified quality level, the GE algorithm works in the AES (Aggressive Energy Saving) mode for the majority of the time, neglecting the low- quality portions of the workload. When the perceived quality falls below the required level, the algorithm switches to the BQ (Best Quality) mode with a compensation policy. To avoid core speed thrashing between the two modes, GE employs a hybrid power distribution scheme that uses the Equal-Sharing (ES) policy to distribute power among the cores when the workload is light (to save energy) and the Water-Filling (WF) policy when the workload is high (to improve quality). We conduct simulations to compare the performance of GE with existing scheduling algorithms. Results show that the proposed algorithm can provide large energy savings with satisfactory user experience.
AB - Power is a primary concern for mobile, cloud, and high-performance computing applications. Approximate computing refers to running applications to obtain results with tolerable errors under resource constraints, and it can be applied to balance energy consumption with service quality. In this paper, we propose a 'Good Enough (GE)' scheduling algorithm that uses approximate computing to provide satis- factory QoS (Quality of Service) for interactive applications with significant energy savings. Given a user-specified quality level, the GE algorithm works in the AES (Aggressive Energy Saving) mode for the majority of the time, neglecting the low- quality portions of the workload. When the perceived quality falls below the required level, the algorithm switches to the BQ (Best Quality) mode with a compensation policy. To avoid core speed thrashing between the two modes, GE employs a hybrid power distribution scheme that uses the Equal-Sharing (ES) policy to distribute power among the cores when the workload is light (to save energy) and the Water-Filling (WF) policy when the workload is high (to improve quality). We conduct simulations to compare the performance of GE with existing scheduling algorithms. Results show that the proposed algorithm can provide large energy savings with satisfactory user experience.
KW - approximate computing
KW - multicore servers
KW - power efficiency
KW - scheduling algorithm
UR - http://www.scopus.com/inward/record.url?scp=85028053880&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028053880&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2017.38
DO - 10.1109/IPDPSW.2017.38
M3 - Conference contribution
AN - SCOPUS:85028053880
T3 - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
SP - 984
EP - 993
BT - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2017 through 2 June 2017
ER -