Performance model building of pervasive computing

Performance model building is essential to predict the ability of an application to satisfy given levels of performance or to support the search for viable alternatives. Using automated methods of model building is becoming of increasing interest to software developers who have neither the skills nor the time to do it manually. This is particularly relevant in pervasive computing, where the large number of software and hardware components requires models of so large a size that using traditional manual methods of model building would be error prone and time consuming. This paper deals with an automated method to build performance models of pervasive computing applications, which require the integration of multiple technologies, including software layers, hardware platforms and wired/wireless networks. The considered performance models are of extended queueing network (EQN) type. The method is based on a procedure that receives as input the UML model of the application to yield as output the complete EQN model, which can then be evaluated by use of any evaluation tool.