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This paper presents a machine learning–driven approach to cloud resource allocation that combines LSTM-based demand forecasting with reinforcement learning (DQN) for dynamic scheduling. The problem is well motivated, as efficient resource management remains central to improving both performance and cost efficiency in large-scale cloud computing environments. By integrating predictive and adaptive components, the proposed system addresses short-term demand variability while enabling real-time allocation decisions.

A notable strength of the work is its empirical evaluation, which reports substantial gains in resource utilization, response time, and operational cost reduction in a production cloud setting. These results suggest that the combined forecasting-and-control approach can outperform static or reactive allocation strategies. The paper also demonstrates the practical applicability of learning-based resource management beyond simulation-based studies.

However, the evaluation would benefit from clearer discussion of workload characteristics, system baselines, and training overheads associated with the learning models. Additional comparison with alternative forecasting or reinforcement learning approaches would strengthen the claims of generality. Furthermore, the long-term stability and adaptability of the system under changing workload distributions are not fully explored.

Overall, this paper contributes a pragmatic and scalable solution to intelligent cloud resource optimization and provides useful evidence supporting the role of machine learning in cost- and performance-aware cloud management.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.