# Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management

arXiv: https://arxiv.org/abs/2402.17216 · HTML: https://mlsanity.com/paper/2402.17216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing](https://mlsanity.com/paper/2007.07122/) (2020)
- [Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design](https://mlsanity.com/paper/2003.00199/) (2020)
- [Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms](https://mlsanity.com/paper/2312.12872/) (2023)
- [Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method](https://mlsanity.com/paper/2401.06782/) (2024)
- [How to Place Your Apps in the Fog -- State of the Art and Open Challenges](https://mlsanity.com/paper/1901.05717/) (2019)

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

- [GPU Memory Requirement Prediction for Deep Learning Task Based on Bidirectional Gated Recurrent Unit Optimization Transformer](https://mlsanity.com/paper/2510.20985/) (2025)
- [The intelligent prediction and assessment of financial information risk in the cloud computing model](https://mlsanity.com/paper/2404.09322/) (2024)
- [Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm Improved by Vector Weighted Average Algorithm](https://mlsanity.com/paper/2503.04088/) (2025)
- [Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery](https://mlsanity.com/paper/2507.16229/) (2025)

## Abstract (arXiv)

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan. At present, the more mature cloud service providers in China are Ali Cloud, Baidu Cloud, Huawei Cloud and so on. Therefore, this paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource management.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.