Performance by Design: Computer Capacity Planning By Example
Performance by Design: Computer Capacity Planning By Example
A Style-Aware Architectural Middleware for Resource-Constrained, Distributed Systems
IEEE Transactions on Software Engineering
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
Leveraging Resource Prediction for Anticipatory Dynamic Configuration
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
RELAX: Incorporating Uncertainty into the Specification of Self-Adaptive Systems
RE '09 Proceedings of the 2009 17th IEEE International Requirements Engineering Conference, RE
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
Scenario-driven dynamic analysis of distributed architectures
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
FUSION: a framework for engineering self-tuning self-adaptive software systems
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Software engineering in an uncertain world
Proceedings of the FSE/SDP workshop on Future of software engineering research
Taming uncertainty in self-adaptive software
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
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Self-adaptation endows a software system with the ability to satisfy certain objectives by automatically modifying its behavior. While many promising approaches for the construction of self-adaptive software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation decisions. This has been one of the key inhibitors to widespread adoption of self-adaption techniques in risk-averse real-world settings. In this research abstract I outline my ongoing effort in the development of a framework for managing uncertainty in self-adaptation. This framework employs state-of-the-art mathematical approaches to model and assess uncertainty in adaptation decisions. Preliminary results show that knowledge about uncertainty allows self-adaptive software systems to make better decisions.