# Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems

arXiv: https://arxiv.org/abs/2410.11862 · HTML: https://mlsanity.com/paper/2410.11862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 2 of 2)

- [Automated Cloud Provisioning on AWS using Deep Reinforcement Learning](https://mlsanity.com/paper/1709.04305/) (2017)
- [Power-Performance Tradeoffs in Data Center Servers: DVFS, CPU pinning, Horizontal, and Vertical Scaling](https://mlsanity.com/paper/1903.05488/) (2019)

## Abstract (arXiv)

Given its intuitive nature, many Cloud providers opt for threshold-based data replication to enable automatic resource scaling. However, setting thresholds effectively needs human intervention to calibrate thresholds for each metric and requires a deep knowledge of current workload trends, which can be challenging to achieve. Reinforcement learning is used in many areas related to the Cloud Computing, and it is a promising field to get automatic data replication strategies. In this work, we survey data replication strategies and data scaling based on reinforcement learning (RL).