# Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions

arXiv: https://arxiv.org/abs/2105.05079 · HTML: https://mlsanity.com/paper/2105.05079/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 152 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 23 of 23)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints](https://mlsanity.com/paper/2001.06720/) (2020)
- [Multichannel Variable-Size Convolution for Sentence Classification](https://mlsanity.com/paper/1603.04513/) (2016)
- [Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning](https://mlsanity.com/paper/2011.03649/) (2020)
- [A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade](https://mlsanity.com/paper/1711.09123/) (2017)
- [Reducing Artificial Neural Network Complexity: A Case Study on Exoplanet Detection](https://mlsanity.com/paper/1902.10385/) (2019)
- [Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems](https://mlsanity.com/paper/2006.05075/) (2020)
- [Machine Learning and Cloud Computing: Survey of Distributed and SaaS Solutions](https://mlsanity.com/paper/1603.08767/) (2016)
- [A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing](https://mlsanity.com/paper/1607.06269/) (2016)
- [Modeling Conceptual Characteristics of Virtual Machines for CPU Utilization Prediction](https://mlsanity.com/paper/1811.04731/) (2018)

## Built on by (most cited first, 4 of 4)

- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management](https://mlsanity.com/paper/2205.08072/) (2022)
- [Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation](https://mlsanity.com/paper/2106.12178/) (2021)
- [SLA-Centric Automated Algorithm Selection Framework for Cloud Environments](https://mlsanity.com/paper/2507.21963/) (2025)

## Abstract (arXiv)

Cloud computing has rapidly emerged as model for delivering Internet-based utility computing services. In cloud computing, Infrastructure as a Service (IaaS) is one of the most important and rapidly growing fields. Cloud providers provide users/machines resources such as virtual machines, raw (block) storage, firewalls, load balancers, and network devices in this service model. One of the most important aspects of cloud computing for IaaS is resource management. Scalability, quality of service, optimum utility, reduced overheads, increased throughput, reduced latency, specialised environment, cost effectiveness, and a streamlined interface are some of the advantages of resource management for IaaS in cloud computing. Traditionally, resource management has been done through static policies, which impose certain limitations in various dynamic scenarios, prompting cloud service providers to adopt data-driven, machine-learning-based approaches. Machine learning is being used to handle a variety of resource management tasks, including workload estimation, task scheduling, VM consolidation, resource optimization, and energy optimization, among others. This paper provides a detailed review of challenges in ML-based resource management in current research, as well as current approaches to resolve these challenges, as well as their advantages and limitations. Finally, we propose potential future research directions based on identified challenges and limitations in current research.