Performance prediction in production environments

Accurate performance predictions are difficult to achieve for parallel applications executing on production distributed systems. Conventional point-valued performance parameters and prediction models are often inaccurate since they can only represent one point in a range of possible behaviors. The authors address this problem by allowing characteristic application and system data to be represented by a set of possible values and their probabilities, which they call stochastic values. They give a practical methodology for using stochastic values as parameters to adaptable performance prediction models. They demonstrate their usefulness for a distributed SOR application, showing stochastic values to be more effective than single (point) values in predicting the range of application behavior that can occur during execution in production environments.