  As the use of cloud computing continues to rise, controlling cost becomes
increasingly important. Yet there is evidence that 30\% - 45\% of cloud spend
is wasted. Existing tools for cloud provisioning typically rely on highly
trained human experts to specify what to monitor, thresholds for triggering
action, and actions. In this paper we explore the use of reinforcement learning
(RL) to acquire policies to balance performance and spend, allowing humans to
specify what they want as opposed to how to do it, minimizing the need for
cloud expertise. Empirical results with tabular, deep, and dueling double deep
Q-learning with the CloudSim simulator show the utility of RL and the relative
merits of the approaches. We also demonstrate effective policy transfer
learning from an extremely simple simulator to CloudSim, with the next step
being transfer from CloudSim to an Amazon Web Services physical environment.
