Automated Cloud Provisioning on AWS using Deep Reinforcement Learning
This addresses cost control for cloud users by reducing reliance on expert knowledge, though it is incremental as it builds on existing RL methods.
The paper tackles the problem of wasted cloud spending (30-45%) by using reinforcement learning to automate cloud provisioning on AWS, achieving effective policy transfer from simulators to physical environments.
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.