Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization
This work addresses cost optimization in cloud resource management for practitioners, but it is incremental as it combines existing methods.
The paper tackles the problem of high costs from over-provisioning in cloud computing due to dynamic workloads by proposing a hybrid framework that combines LSTM-based predictive scaling with heuristic task allocation, resulting in reduced infrastructure costs close to ML-based models while maintaining fast response times similar to heuristic methods.
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.