A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A recursive random search algorithm for large-scale network parameter configuration
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Comparisons with a Standard in Simulation Experiments
Management Science
A smart hill-climbing algorithm for application server configuration
Proceedings of the 13th international conference on World Wide Web
Finding probably best system configurations quickly
ACM SIGMETRICS Performance Evaluation Review
Optimizing system configurations quickly by guessing at the performance
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Finding probably best systems quickly via simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
MassConf: automatic configuration tuning by leveraging user community information
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
scc: cluster storage provisioning informed by application characteristics and SLAs
FAST'12 Proceedings of the 10th USENIX conference on File and Storage Technologies
| Hi-index | 0.00 |
The performance of computer and communication systems can in theory be optimized by iteratively finding better system configurations. However, a bottleneck is the time required in simulations/experiments for finding a better system configuration in each iteration. We propose algorithms that quickly find a system configuration that is probably better than the "standard" system configuration, where the performance of a given system configuration is estimated via simulations or experiments. We prove that our algorithms make correct decisions with high probability, and various heuristics to reduce the total simulation time are proposed. Numerical experiments show the effectiveness of the proposed algorithms, and this leads to several guidelines for designing efficient and reliable optimization procedures for the performance of computer and communication systems.