TY - GEN
T1 - A response surface methodology approach to operating system scheduler tuning
AU - Anderson, George
AU - Marwala, Tshilidzi
AU - Nelwamondo, Fulufhelo Vincent
PY - 2010
Y1 - 2010
N2 - Tuning operating system components is a cyclical process involving setting parameters, evaluating the effect of the settings, making adjustments, and testing again. This is an expensive process, both taking a long time and requiring money to hire people to do it. In this paper we present a statistical approach to tuning of an operating system scheduler using Design of Experiments (DOE) and Response Surface Methodology (RSM). We make use of a benchmark and generate a response surface based on the runtime of the benchmark and three Linux scheduler parameters. We produce a model of the scheduler and optimize the parameter settings, minimizing the number of times the benchmark had to be run to find the optimal settings. In our experiment, we achieved an 11% performance improvement when the Linux scheduler ruus the benchmark. We also compared the scalability of the optimized and unoptimized schedulers and discovered that the optimized scheduler does much better in this regard. This was done without prior knowledge of optimal settings for the workload we used.
AB - Tuning operating system components is a cyclical process involving setting parameters, evaluating the effect of the settings, making adjustments, and testing again. This is an expensive process, both taking a long time and requiring money to hire people to do it. In this paper we present a statistical approach to tuning of an operating system scheduler using Design of Experiments (DOE) and Response Surface Methodology (RSM). We make use of a benchmark and generate a response surface based on the runtime of the benchmark and three Linux scheduler parameters. We produce a model of the scheduler and optimize the parameter settings, minimizing the number of times the benchmark had to be run to find the optimal settings. In our experiment, we achieved an 11% performance improvement when the Linux scheduler ruus the benchmark. We also compared the scalability of the optimized and unoptimized schedulers and discovered that the optimized scheduler does much better in this regard. This was done without prior knowledge of optimal settings for the workload we used.
KW - Operating systems
KW - Response surface methodology
KW - Scheduling
KW - System modeling & control
UR - http://www.scopus.com/inward/record.url?scp=78751541961&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2010.5641890
DO - 10.1109/ICSMC.2010.5641890
M3 - Conference contribution
AN - SCOPUS:78751541961
SN - 9781424465880
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2684
EP - 2689
BT - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
T2 - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Y2 - 10 October 2010 through 13 October 2010
ER -