Defuzzification: criteria and classification
Fuzzy Sets and Systems
The Vision of Autonomic Computing
Computer
Fuzzy Recommendation towards QoS-Aware Pervasive Learning
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
Bringing Semantics to Web Services with OWL-S
World Wide Web
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
Cholla: A Framework for Composing and Coordinating Adaptations in Networked Systems
IEEE Transactions on Computers
Composition of Qualitative Adaptation Policies
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
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
Flicker effects in adaptive video streaming to handheld devices
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Offering streaming rate adaptation to common media players
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
A control-based middleware framework for quality-of-service adaptations
IEEE Journal on Selected Areas in Communications
Layered quality adaptation for Internet video streaming
IEEE Journal on Selected Areas in Communications
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
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Adaptation is an increasingly important requirement for software systems executing in large-scale, heterogeneous, and dynamic environments. A central aspect of the adaptation methodology is management of contextual information needed to support the adaptation process. A major design challenge of managing contextual data lies in the fact that the information is partial, uncertain, and inherently suitable for diverging interpretations. While existing adaptation solutions focus on techniques, methods, and tools, the challenge of managing and interpreting ambiguous contextual information remains largely unresolved. In this paper, we present a new adaptation approach that aims to overcome these issues by applying fuzzy set theory and approximate reasoning. It proposes a knowledge management scheme to interpret imprecise information and effectively integrate this information into the adaptation feedback control loop. To test and evaluate our solution, we implemented it in an adaptation engine to perform rate control for media streaming applications. The evaluation results show the benefits of our approach in terms of flexibility and performance when compared to more traditional methods, such as TCP-friendly rate control.