mlsanityData to 14 Oct 2026

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

arXiv 2410.11862 ↗First posted 7 Oct 2024cs.DC cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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).

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.

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2 of 37 references.

References · 2

  1. 2017Automated Cloud Provisioning on AWS using Deep Reinforcement Learningcited by 2
  2. 2019Power-Performance Tradeoffs in Data Center Servers: DVFS, CPU pinning, Horizontal, and Vertical Scalingcited by 2

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