Performance measurement and data base design
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
We propose an indifference-zone approach for a ranking and selection problem with the goal of reducing both the number of simulated samples of the performance and the frequency of configuration changes. We prove that with a prespecified high probability, our algorithm finds the best system configuration. Our proof hinges on several ideas, including the use of Anderson's probability bound, that have not been fully investigated for the ranking and selection problem. Numerical experiments show that our algorithm can select the best system configuration using up to 50% fewer simulated samples than existing algorithms without increasing the frequency of configuration changes. © 2009 ACM.
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
Khaled A.S. Abdel-Ghaffar
IEEE Trans. Inf. Theory
Yigal Hoffner, Simon Field, et al.
EDOC 2004
Rajeev Gupta, Shourya Roy, et al.
ICAC 2006