Architecture-based resilience evaluation for self-adaptive systems

One of the major challenges related to self-adaptive software systems is the provision of assurances that the system is resilient against changes that may occur either in the system or its environment. These assurances should be based on complementary sources of evidence that collectively justify that the system is able to attain the specified levels of resilience. The contribution of this paper is the definition and development of an architecture-based approach that evaluates by comparison the adaptation mechanisms of a self-adaptive software system. The proposed approach relies on the identification of representative environmental and system changeloads (i.e., sequences of changes) used in the run-time stimulation of the system. The system response obtained from this stimulation is collected and aggregated into a probabilistic model that is employed in the evaluation of system resilience. Our approach is intended to be used before deployment, since the process often involves putting the system through adverse conditions which are not adequate when the system is in production. The feasibility and effectiveness of the proposed approach is demonstrated in the context of Rainbow, an architecture-based platform for self- adaptation, and Znn.com, a case study that reproduces the typical infrastructure for a news website.