1. A method for predicting performance of an application on a server of a predetermined hardware configuration, the method comprising: simulating, in a computer process, the performance of the application under a plurality of different simulated server hardware configurations, each of the simulated performances of the application measured in average CPU cycles per instruction; building, in a computer process, a predictive model for predicting the performance of the application on a server with any hardware configuration based on the results of the simulations; obtaining actual performances of the application on a plurality of actual servers, each of the servers having a different hardware configuration, each of the actual performances of the application measured in average CPU cycles per instruction; and in a computer process, Bayesian reinterpreting the predictive model built from the results of the simulations using the actual performances of the application on the plurality of actual servers to obtain a final predictive model θ* of the performance of the application having an accuracy greater than the predictive model built from the results of the simulations, the final predictive model θ* comprising a weighted average of prior predictive model θ and predictive model {circumflex over (θ)} obtained from a least square solution, the final predictive model θ* being equal to ({tilde over (φ)} T {tilde over (φ)}+Σ −1 ) −1 ({tilde over (φ)} T {tilde over (φ)}{circumflex over (θ)}+Σ −1 θ ), wherein {tilde over (φ)} T {tilde over (φ)} and Σ are weights comprising a data matrix and a covariance, respectively.