Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

TL;DR

This paper investigates using deep reinforcement learning to automate cloud resource provisioning on AWS, aiming to reduce costs and minimize human expertise needed, validated through simulation and transfer learning.

Contribution

It introduces RL-based policies for cloud provisioning that require less human input and demonstrates transfer learning from simple simulators to realistic cloud environments.

Findings

RL approaches effectively balance performance and cost.

Transfer learning reduces the need for extensive retraining.

Deep Q-learning variants outperform traditional methods.

Abstract

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.