# Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions

arXiv: https://arxiv.org/abs/2107.02342 · HTML: https://mlsanity.com/paper/2107.02342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 139 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Device Placement Optimization with Reinforcement Learning](https://mlsanity.com/paper/1706.04972/) (2017)
- [Serverless Computing: Current Trends and Open Problems](https://mlsanity.com/paper/1706.03178/) (2017)
- [A Survey of Methods For Analyzing and Improving GPU Energy Efficiency](https://mlsanity.com/paper/1404.4629/) (2014)
- [A Survey on Domain-Specific Languages for Machine Learning in Big Data](https://mlsanity.com/paper/1602.07637/) (2016)
- [Fog Computing: A Taxonomy, Survey and Future Directions](https://mlsanity.com/paper/1611.05539/) (2016)
- [Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning](https://mlsanity.com/paper/2011.03649/) (2020)
- [A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View](https://mlsanity.com/paper/1712.02899/) (2017)

## Abstract (arXiv)

This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency. It identifies the need for integrated computing and cooling systems management and learning-based solutions in resource management systems. A taxonomy on energy and thermal efficient resource management in data centres is proposed based on an in-depth analysis of the literature. Furthermore, a detailed survey on existing approaches is conducted according to the taxonomy and recent advancements including machine learning-based resource management approaches and cooling management technologies are discussed.