Learning automata: an introduction
Learning automata: an introduction
Importance sampling for stochastic simulations
Management Science
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Using Architecture Models for Runtime Adaptability
IEEE Software
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference
Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference
A comprehensive solution for application-level adaptation
Software—Practice & Experience
Computer
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
Solving multiconstraint assignment problems using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of context modelling and reasoning techniques
Pervasive and Mobile Computing
Online Stochastic Combinatorial Optimization
Online Stochastic Combinatorial Optimization
A survey of automated web service composition methods
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
An aspect-oriented approach for developing self-adaptive fractal components
SC'06 Proceedings of the 5th international conference on Software Composition
Journal of Systems and Software
A vision for better cloud applications
Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds
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Applications deployed in multi-clouds will face issues like where to deploy the different artefacts, how to scale the application in case of performance problems, and how to adapt the application deployment. For complex applications it may be difficult to find manually the best allocation of the artefacts on the available infrastructures. This paper presents a vision for an autonomic deployment system. In particular, it details the architecture of a learning automata based reasoning component envisioned to be able to provide feasible allocations and discusses the research challenges originating from this approach.