Automated optimization of weighted non-functional objectives in self-adaptive systems

Abstract

A self-adaptive system (SAS) can reconfigure at run time in response to adverse combinations of system and environmental conditions in order to continuously satisfy its requirements. Moreover, SASs are subject to cross-cutting non-functional requirements (NFRs), such as performance, security, and usability, that collectively characterize how functional requirements (FRs) are to be satisfied. In many cases, the trigger for adapting an SAS may be due to a violation of one or more NFRs. For a given NFR, different combinations of hierarchically-organized FRs may yield varying degrees of satisfaction (i.e., satisficement). This paper presents Providentia, a search-based technique to optimize NFR satisficement when subjected to various sources of uncertainty (e.g., environment, interactions between system elements, etc.). Providentia searches for optimal combinations of FRs that, when considered with different subgoal decompositions and/or differential weights, provide optimal satisficement of NFR objectives. Experimental results suggest that using an SAS goal model enhanced with search-based optimization significantly improves system performance when compared with manually- and randomly-generated weights and subgoals.