TY - GEN
T1 - Performance optimization and regulation for multitier servers
AU - Luna, Jose Marcio
AU - Abdallah, Chaouki T.
AU - Heileman, Gregory L.
N1 - Funding Information:
ACKNOWLEDGMENT: This work is partially supported by the Ministry of Science and Technology under the Grant MOST 103-2221-E-182-060-MY2, Taiwan.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - In this paper we present a novel approach to optimize and regulate performance in a multitier server. By using a queueing network model, we optimize the values of the mean service rates at each tier in the server by applying randomized algorithms based on statistical learning theory. After the optimization process is carried out, an IPA algorithm is implemented to regulate the throughput of the system to the calculated optimal reference value in a closed loop. The case study of a server with three tiers is simulated to validate our approach.
AB - In this paper we present a novel approach to optimize and regulate performance in a multitier server. By using a queueing network model, we optimize the values of the mean service rates at each tier in the server by applying randomized algorithms based on statistical learning theory. After the optimization process is carried out, an IPA algorithm is implemented to regulate the throughput of the system to the calculated optimal reference value in a closed loop. The case study of a server with three tiers is simulated to validate our approach.
UR - http://www.scopus.com/inward/record.url?scp=84961999688&partnerID=8YFLogxK
U2 - 10.1109/CDC.2015.7402007
DO - 10.1109/CDC.2015.7402007
M3 - Conference contribution
AN - SCOPUS:84961999688
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1026
EP - 1032
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -