# Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization

arXiv: https://arxiv.org/abs/2604.02131 · HTML: https://mlsanity.com/paper/2604.02131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 2 Apr 2026.

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

- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning](https://mlsanity.com/paper/2504.03682/) (2025)
- [FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud](https://mlsanity.com/paper/2505.21727/) (2025)
- [Simulating Dynamic Cloud Marketspaces: Modeling Spot Instance Behavior and Scheduling with CloudSim Plus](https://mlsanity.com/paper/2511.18137/) (2025)
- [Opportunistic Scheduling for Optimal Spot Instance Savings in the Cloud](https://mlsanity.com/paper/2601.12266/) (2026)
- [Exploiting Spot Instances for Time-Critical Cloud Workloads Using Optimal Randomized Strategies](https://mlsanity.com/paper/2601.14612/) (2026)

## Abstract (arXiv)

Cloud computing allows scalable resource provisioning, but dynamic workload changes often lead to higher costs due to over-provisioning. Machine learning (ML) approaches, such as Long Short-Term Memory (LSTM) networks, are effective for predicting workload patterns at a higher level, but they can introduce delays during sudden traffic spikes. In contrast, mathematical heuristics like Game Theory provide fast and reliable scheduling decisions, but they do not account for future workload changes. To address this trade-off, this paper proposes a hybrid orchestration framework that combines LSTM-based predictive scaling with heuristic task allocation. The results show that this approach reduces infrastructure costs close to ML-based models while maintaining fast response times similar to heuristic methods. This work presents a practical approach for improving cost efficiency in cloud resource management.