A tutorial review of techniques for simulation optimization
WSC '94 Proceedings of the 26th conference on Winter simulation
A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
A revised simplex search procedure for stochastic simulation response-surface optimization
Proceedings of the 30th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Design of a performance technology infrastructure to support the construction of responsive software
Proceedings of the 2nd international workshop on Software and performance
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
A framework for Response Surface Methodology for simulation optimization
Proceedings of the 32nd conference on Winter simulation
A framework for distributed simulation optimization
Proceedings of the 33nd conference on Winter simulation
Panel: simulation optimization: future of simulation optimization
Proceedings of the 33nd conference on Winter simulation
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A common problem that sales consultants face in the field is the selection of an appropriate hardware and software configuration for web farms. Over-provisioning means that the tender will be expensive while under-provisioning will lead to a configuration that does not meet the customer criteria. Indy is a performance modeling environment which allows developers to create custom modeling applications. We have constructed an Indy-based application for defining web farm workloads and topologies. The paper presents an optimization framework that allows the consultant to easily find configurations that meet customers' criteria. The system searches the solution space creating possible configurations, using the web farm models to predict their performance. The optimization tool is then employed to select an optimal configuration. Rather than using a fixed algorithm, the framework provides an infrastructure for implementing multiple optimization algorithms. In this way, the appropriate algorithm can be selected to match the requirements of different types of problem. The framework incorporates a number of novel techniques, including caching results between problem runs, an XML based configuration language, and an effective method of comparing configurations. We have applied the system to a typical web farm configuration problem and results have been obtained for three standard optimization algorithms.