FUSION: a framework for engineering self-tuning self-adaptive software systems
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Stitch: A language for architecture-based self-adaptation
Journal of Systems and Software
Learning revised models for planning in adaptive systems
Proceedings of the 2013 International Conference on Software Engineering
Lifecycle of adaptive agreements: a pattern language
AT'13 Proceedings of the Second international conference on Agreement Technologies
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Recently, software systems face dynamically changing environments, and the users of the systems provide changing requirements at run-time. Self-management is emerging to deal with these problems. One of the key issues to achieve self-management is planning for selecting appropriate structure or behavior of self-managed software systems. There are two types of planning in self-management: off-line and on-line planning. Recent discussion has focused on off-line planning which provides static relationships between environmental changes and software configurations. In on-line planning, a software system can autonomously derive mappings between environmental changes and software configurations by learning its dynamic environment and using its prior experience. In this paper, we propose a reinforcement learning-based approach to on-line planning in architecture-based self-management. This approach enables a software system to improve its behavior by learning the results of its behavior and by dynamically changing its plans based on the learning in the presence of environmental changes. The paper presents a case study to illustrate the approach and its result shows that reinforcement learning-based on-line planning is effective for architecture-based self-management.