# Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning

arXiv: https://arxiv.org/abs/2504.03682 · HTML: https://mlsanity.com/paper/2504.03682/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments](https://mlsanity.com/paper/2512.22149/) (2025)
- [Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization](https://mlsanity.com/paper/2604.02131/) (2026)

## Abstract (arXiv)

With the rapid expansion of cloud computing applications, optimizing resource allocation has become crucial for improving system performance and cost efficiency. This paper proposes an intelligent resource allocation algorithm that leverages deep learning (LSTM) for demand prediction and reinforcement learning (DQN) for dynamic scheduling. By accurately forecasting computing resource demands and enabling real-time adjustments, the proposed system enhances resource utilization by 32.5%, reduces average response time by 43.3%, and lowers operational costs by 26.6%. Experimental results in a production cloud environment confirm that the method significantly improves efficiency while maintaining high service quality. This study provides a scalable and effective solution for intelligent cloud resource management, offering valuable insights for future cloud optimization strategies.