The following needs to be updated. Please contact <ahref="http://se.informatik.uni-wuerzburg.de/staff/juergen_walter/">Jürgen Walter<\a> in case of questions.
## Introduction
Over the past few decades, many performance modeling formalisms and prediction techniques for software architectures have been developed in the performance engineering community. However, using a performance model to predict the performance of a software system normally requires extensive experience with the respective modeling formalism and involves a number of complex and time consuming manual steps. Our approach is a generic declarative interface to performance prediction techniques that simplifies and automates the process of using architecture-level software performance models for performance analysis. The Descartes Query Language (DQL) is a language to express the demanded performance metrics for prediction as well as the goals and constraints in the specific prediction scenario[1]. It reduces the manual effort and the learning curve when working with performance models by a unified interface independent of the employed modeling formalism. DQL is part of our ongoing work towards self-aware computing systems and to provide means to cope with the growing complexity through the increasing amount of deployed software systems, integrated abstraction layers and required computing resources under operation in modern dynamic data centers.
