Conference Proceedings


Giovani Estrada
Andreas Metzger
Claus Pahl
Amir Sharifloo
Pooyan Jamshidi



cloud controller self learning fuzzy logic elasticity cloud fuzzy controllers q learning software engineering

Self-learning cloud controllers: fuzzy Q-learning for knowledge evolution (2015)

Abstract Auto-scaling features enable cloud applications to maintain enough resources to satisfy demand spikes, reduce costs and keep performance in check. Most auto-scaling strategies rely on a predefined set of rules to scale up/down the required resources depending on the application usage. Those rules are however difficult to devise and generalize, and users are often left alone tuning auto-scale parameters of essentially blackbox applications. In this paper, we propose a novel fuzzy reinforcement learning controller, FQL4KE, which automatically scales up or down resources to meet performance requirements. The Q-Learning technique, a model-free reinforcement learning strategy, frees users of most tuning parameters. FQL4KE has been successfully applied and we therefore think that a fuzzy controller with Q-Learning is indeed a promising combination for auto-scaling resources.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Irish Centre for Cloud Computing and Commerce (IC4)
Ireland -> Dublin City University -> Subject = Computer Science: Software engineering

Full list of authors on original publication

Giovani Estrada, Andreas Metzger, Claus Pahl, Amir Sharifloo, Pooyan Jamshidi

Experts in our system

Claus Pahl
Dublin City University
Total Publications: 191
Pooyan Jamshidi
Dublin City University
Total Publications: 26