The Vision of Autonomic Computing
Computer
Feedback Control of Computing Systems
Feedback Control of Computing Systems
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Rainbow: Architecture-Based Self-Adaptation with Reusable Infrastructure
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Hierarchical model-based autonomic control of software systems
DEAS '05 Proceedings of the 2005 workshop on Design and evolution of autonomic application software
Model-based development of dynamically adaptive software
Proceedings of the 28th international conference on Software engineering
Model-driven Development of Complex Software: A Research Roadmap
FOSE '07 2007 Future of Software Engineering
Genie: supporting the model driven development of reflective, component-based adaptive systems
Proceedings of the 30th international conference on Software engineering
Scala Actors: Unifying thread-based and event-based programming
Theoretical Computer Science
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
Engineering Self-Adaptive Systems through Feedback Loops
Software Engineering for Self-Adaptive Systems
Adaptation and abstract runtime models
Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Making control loops explicit when architecting self-adaptive systems
Proceedings of the second international workshop on Self-organizing architectures
Decision making in autonomic computing systems: comparison of approaches and techniques
Proceedings of the 8th ACM international conference on Autonomic computing
An eclipse modelling framework alternative to meet the models@runtime requirements
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
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Engineering self-adaptive systems is a particularly challenging problem. On the one hand, it is hard to develop the right control model that drives the adaptation; on the other hand, the implementation and integration of this control model into the target system is a difficult and an error-prone activity. Models@runtime is a promising approach to managing adaptations at runtime, as they provide higher levels of abstractions of both the running system and its environment. However, recent work mainly focuses on runtime models that are causally connected to running systems and less attention is paid to how models can be used to develop and manage the control logic that drives runtime adaptations. In this paper we propose an alternative form of models@runtime as a reactive data-driven model centered around feedback control loops. Both the target system and the adaptation logic are represented as networks of message passing actors. Each of these actors represents a particular abstraction over the running system (sensors, effectors) and its control (analysis, decision). Moreover, the actors are also viewed as target systems themselves. This makes the feedback loops adaptable at runtime as well and permits us to build complex solutions with hierarchical layers of control loops. We discuss how this representation fits some of the requirements of models@runtime and helps to prototype a feedback control system on a concrete example extracted from ongoing validation case studies.