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

Researchers Heet Nagoriya and Komal Rohit have published a paper on arXiv titled 'Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization.' The work addresses a critical pain point in cloud computing: the trade-off between cost efficiency and performance. Pure machine learning models, like LSTM networks, are excellent at predicting workload patterns to optimize resource provisioning but can be slow to react to sudden, unexpected traffic spikes. Conversely, mathematical heuristics like those derived from Game Theory can make fast scheduling decisions but lack foresight, often leading to over-provisioning and higher costs.

Their proposed framework intelligently merges these two approaches. The LSTM component handles high-level, predictive scaling based on historical patterns, while the heuristic component provides immediate, reliable task allocation during volatile periods. According to the paper, this hybrid model achieves a best-of-both-worlds outcome. It reduces infrastructure costs to levels comparable with slower, ML-only models while maintaining the fast response times characteristic of heuristic-only systems. This represents a significant step toward more autonomous and cost-effective cloud resource management, potentially saving enterprises substantial operational expenses.