# Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1709.04305 · HTML: https://mlsanity.com/paper/1709.04305/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Adversarial Deep Learning for Online Resource Allocation](https://mlsanity.com/paper/2111.10285/) (2021)
- [Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems](https://mlsanity.com/paper/2410.11862/) (2024)

## Abstract (arXiv)

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.