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

Research output: Contribution to journalArticle

Colleges, School and Institutes

External organisations

  • Department of Computing and Technology, Nottingham Trent University, UK, and CERCIA, School of Computer Science, University of Birmingham, UK
  • School of Computer Science and Engineering, University of Electronic Science and Technology of China, China, and Department of Computer Science, University of Exeter, UK
  • CERCIA, School of Computer Science, The University of Birmingham
  • Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China, and CERCIA, School of Computer Science, University of Birmingham, UK

Abstract

Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers’ design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace; another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes.

Details

Original languageEnglish
Article number2
Number of pages50
JournalACM Transactions on Software Engineering and Methodology
Volume27
Issue number2
Publication statusPublished - 29 Jun 2018