Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow

Dynamic and appropriate resource dimensioning is a crucial issue in cloud computing. As applications go more and more 24/7, online policies must be sought to balance performance with the cost of allocated virtual machines. Most industrial approaches to date use ad hoc manual policies, such as threshold-based ones. Providing good thresholds proved to be tricky and hard to automatize to fit every application requirement. Research is being done to apply automatic decision-making approaches, such as reinforcement learning. Yet, they face a lot of problems to go to the field: having good policies in the early phases of learning, time for the learning to converge to an optimal policy and coping with changes in the application performance behavior over time.