Model-based performance analysis of software architectures under uncertainty

Performance analysis is often conducted before achieving full knowledge of a software system, in other words under a certain degree of uncertainty. Uncertainty is particularly critical in the performance domain when it relates to values of parameters such as workload, operational profile, resource demand of services, service time of hardware devices, etc. The goal of this paper is to explicitly consider uncertainty in the performance modelling and analysis process. In particular, we use probabilistic formulation of parameter uncertainties and present a Monte Carlo simulation-based approach to systematically assess the robustness of an architectural model despite its uncertainty. In case of unsatisfactory results, we introduce refactoring actions aimed at generating new software architectural models that better tolerate the uncertainty of parameters. The proposed approach is illustrated on a case study from the e-Health domain.