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Self-adaptive software (SAS) can reconfigure itself to adapt to the changingenvironment at runtime, aiming for continually optimizing conflictednon-functional objectives, e.g., response time, energy consumption, throughputand cost etc. In this paper, we present Feature guided and knEe drivenMulti-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novelframework that automatically synergizes the feature model and Multi-ObjectiveEvolutionary Algorithm (MOEA), to optimize SAS at runtime. FEMOSAA operates intwo phases: at design time, FEMOSAA automatically transposes the engineers'design of SAS, expressed as a feature model, to fit the MOEA, creating newchromosome representation and reproduction operators. At runtime, FEMOSAAutilizes the feature model as domain knowledge to guide the search and furtherextend the MOEA, providing a larger chance for finding better solutions. Inaddition, we have designed a new method to search for the knee solutions, whichcan achieve a balanced trade-off. We comprehensively evaluated FEMOSAA on tworunning SAS: one is a highly complex SAS with various adaptable real-worldsoftware under the realistic workload trace, another is a service-oriented SASthat can be dynamically composed from services. In particular, we compared theeffectiveness and overhead of FEMOSAA against four of its variants and threeother search-based frameworks for SAS under various scenarios, including threecommonly applied MOEAs, two workload patterns and diverse conflicting qualityobjectives. The results reveal the effectiveness of FEMOSAA and its superiorityover the others with high statistical significance and non-trivial effectsizes.

FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software