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

TL;DR

This paper introduces a hybrid cloud orchestration framework combining machine learning and heuristics to optimize costs and response times during workload fluctuations.

Contribution

It proposes a novel hybrid framework that integrates LSTM-based workload prediction with heuristic scheduling for efficient cloud resource management.

Findings

Reduces infrastructure costs close to ML-only models

Maintains fast response times similar to heuristic methods

Demonstrates practical cost efficiency improvements in cloud management

Abstract

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.