Self-learning cloud controllers: Fuzzy Q-learning for knowledge evolution
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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.
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Wang X; Lunesu I; Rikkila J; Matta M; Abrahamsson P (Springer, 2014)Self-organization is one of the key agile principles. How it can be applied in an educational context is not explored extensively. In this paper we draw on relevant educational literature as the theoretical basis to ...