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.

翻译：云计算支持弹性资源调配，但动态工作负载变化常因过度配置导致成本上升。诸如长短期记忆网络（LSTM）等机器学习方法虽能有效预测高层级工作负载模式，但在突发流量峰值时可能引入延迟。相比之下，博弈论等数学启发式方法可提供快速可靠的调度决策，却无法预判未来工作负载变化。为平衡这一权衡，本文提出一种混合编排框架，融合基于LSTM的预测性扩缩容与启发式任务分配。实验结果表明，该方法在将基础设施成本降低至接近ML模型水平的同时，仍能保持类似于启发式方法的快速响应时间。本研究为提升云资源管理中的成本效益提供了切实可行的方案。