Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Future Generation Computer Systems
Introduction to Multiagent Systems
Introduction to Multiagent Systems
The Autonomic Computing Paradigm
Cluster Computing
IT service management architecture and autonomic computing
IBM Systems Journal
Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
Modeling Dimensions of Self-Adaptive Software Systems
Software Engineering for Self-Adaptive Systems
Engineering Self-Adaptive Systems through Feedback Loops
Software Engineering for Self-Adaptive Systems
Design patterns for developing dynamically adaptive systems
Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Weaving the fabric of the control loop through aspects
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
On Self-Adaptation, Self-Expression, and Self-Awareness in Autonomic Service Component Ensembles
SASOW '11 Proceedings of the 2011 Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops
FORMS: Unifying reference model for formal specification of distributed self-adaptive systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
SOTA: Towards a General Model for Self-Adaptive Systems
WETICE '12 Proceedings of the 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
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Autonomic systems are able to adapt themselves to unpredicted and unexpected situations. Such adaptation capabilities can reside in individual components as well as in ensembles of components. In particular, a variety of different architectural patterns can be conceived to support self-adaptation at the level both of components and of ensembles. In this paper, we propose a classification of such self-adaptation patterns -- for both the component level and the system level -- by means of a taxonomy organized around the locus in which the feedback loops promoting adaptation reside. We show that the proposed classification covers most self-adaptation patterns, and enables deriving further ones by applying a simple set of composition mechanisms. Three examples of existing patterns of the taxonomy are detailed in the paper to show the applicability of the approach. As discussed in the paper, the advantage of the proposed classification is twofold: it enables identifying the (possibly common) properties of the existing self-adaptation patterns; and, consequently, it can help developers in choosing the most appropriate self-adaptation patterns for the development of autonomic systems.