Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization
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 reduc...
| Authors | Heet Nagoriya, Komal Rohit |
|---|---|
| Primary category | cs.DC |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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