Sciweavers

Share
ICPPW
2009
IEEE

Fast Autotuning Configurations of Parameters in Distributed Computing Systems Using Ordinal Optimization

11 years 8 months ago
Fast Autotuning Configurations of Parameters in Distributed Computing Systems Using Ordinal Optimization
Conventional autotuning configuration of parameters in distributed computing systems using evolutionary strategies increases integrated performance notably, though at the expense of consuming too much measurement time. An ordinal optimization (OO) based strategy is proposed in this work, combined with neural networks to improve system performance and reduce measurement time, which is fast enough to autotune configurations for distributed computing applications. The method is compared with a well known evolutionary algorithm called Covariance Matrix Algorithm (CMA). Experiments are carried out using high dimensional rastrigin functions, which show that OO can reduce one to two orders of magnitude of simulation time while at the cost of an acceptable scope of optimization performance. We also carried out experiments using a real application system with three-tier web servers. Experimental results show that OO can reduce 40% testing time on average at a reasonable cost of optimization pe...
Fan Zhang, Junwei Cao, Lianchen Liu, Cheng Wu
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICPPW
Authors Fan Zhang, Junwei Cao, Lianchen Liu, Cheng Wu
Comments (0)
books