用深度学习和强化学习实现云资源智能调度，提升效率降低成本
Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning
- 用LSTM预测资源需求，DQN动态调度
- 资源利用率提升32.5%，响应时间降低43.3%
- 适合云平台运维与智能调度研究者
随着云计算应用的快速发展，优化资源分配对提升系统性能和成本效益至关重要。本文提出一种基于深度学习（LSTM）的需求预测与强化学习（DQN）动态调度相结合的智能资源分配算法。通过精准预测计算资源需求并实现实时调整，该系统在生产环境中的实验结果显示，资源利用率提升32.5%，平均响应时间降低43.3%，运营成本下降26.6%。结果表明，该方法显著提升了系统效率，同时保持了高质量服务。本研究为智能云资源管理提供了可扩展、高效的解决方案，为未来云优化策略提供重要参考。
原文摘要 · Abstract (English)
With the rapid expansion of cloud computing applications, optimizing resource allocation has become crucial for improving system performance and cost efficiency. This paper proposes an intelligent resource allocation algorithm that leverages deep learning (LSTM) for demand prediction and reinforcement learning (DQN) for dynamic scheduling. By accurately forecasting computing resource demands and enabling real-time adjustments, the proposed system enhances resource utilization by 32.5%, reduces average response time by 43.3%, and lowers operational costs by 26.6%. Experimental results in a production cloud environment confirm that the method significantly improves efficiency while maintaining high service quality. This study provides a scalable and effective solution for intelligent cloud resource management, offering valuable insights for future cloud optimization strategies.
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