A Search-Based Approach for Architectural Design of Feedback Control Concerns in Self-Adaptive Systems

Abstract

A number of approaches for endowing systems with self-adaptive behavior have been proposed over the past years. Among such efforts, architecture-centric solutions with explicit representation of feedback loops have currently been advocated as a promising research landscape. Although noteworthy results have been achieved on some fronts, the lack of systematic representations of architectural knowledge and effective support for well-informed trade-off decisions still poses significant challenges when designing modern self-adaptive systems. In this paper, we present a systematic and flexible representation of design dimensions related to feedback control concerns, a set of metrics which estimate quality attributes of resulting automated designs, and a search-based approach to find out a set of Pareto-optimal candidate architectures. The proposed approach has been fully implemented in a supporting tool and a case study with a self-adaptive cloud computing environment has been undertaken. The results indicate that our approach effectively captures the most prominent degrees of freedom when designing self-adaptive systems, helps to elicit effective subtle designs, and provides useful support for early analysis of trade-off decisions.