An Efficient Sensitivity Analysis Method for Large Cloud Simulations

Simulations of large distributed systems, such as infrastructure clouds, usually entail a large space of parameters and responses that prove impractical to explore. To reduce the space of inputs, experimenters, guided by domain knowledge and ad hoc methods, typically select a subset of parameters and values to simulate. Similarly, experimenters typically use ad hoc methods to reduce the number of responses to analyze. Such ad hoc methods can result in experiment designs that miss significant parameter combinations and important responses, or that overweight selected parameters and responses. When this occurs, the experiment results and subsequent analyses can be misleading. In this paper, we apply an efficient sensitivity analysis method to demonstrate how relevant parameter combinations and behaviors can be identified for an infrastructure Cloud simulator that is intended to compare resource allocation algorithms. Researchers can use the techniques we demonstrate here to design experiments for large Cloud simulations, leading to improved quality in derived research results and findings.