Fuzzy Q-Learning Based Controller for Cost and Energy Efficient Load Balancing in Cloud Data Center

Abstract

The goal of cloud controller is to focus on continuous delivery of services to user on demand basis followed by “pay-per-use” model. Due to the increasing demand of cloud services, energy consumption on data center is increasing rapidly which lead to high operational cost. The harmful emission from this energy intensive data center affects our environment badly and cause climate change significantly. So as an alternative we have focused on onsite green power generation to reduce the harmful effects of greenhouse gases. In this paper, we proposed a fuzzy Q-learning based self-learning controller to optimize the load for specific data center. The proposed method also helps to reduce uncertainty and solve the congestion issue efficiently through fuzzy linguistic behavior and membership function. In this proposal, fuzzy output parameter considered as reward value which is used to learn and update the state for each data centre.