对比10种机器学习算法，发现混合模型更优且适合边缘计算
Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review
- 分四类比较深度强化学习等10种资源分配算法
- 混合方法在完成时间、成本、能耗上均显著优于传统方案
- 适合研究者和企业部署下一代云资源管理策略
云计算资源分配已成为现代计算环境中的主要挑战，组织在应对复杂动态工作负载时难以兼顾性能与成本效率。传统启发式方法难以满足现有云基础设施的多目标优化需求。本文系统比较了当前最先进的人工智能与机器学习算法在资源分配中的应用，评估了10种算法，涵盖深度强化学习、神经网络架构、传统机器学习增强方法及多智能体系统四类。分析显示，相比传统方法，这些算法在完成时间、成本优化和能效提升等多个指标上均有显著改善。研究发现，融合多种人工智能与机器学习技术的混合架构始终优于单一方法，且边缘计算环境展现出最高的部署成熟度。本研究为学术界和工业界在日益复杂动态的计算环境中实施下一代云资源分配策略提供了关键洞见。
原文摘要 · Abstract (English)
Cloud resource allocation has emerged as a major challenge in modern computing environments, with organizations struggling to manage complex, dynamic workloads while optimizing performance and cost efficiency. Traditional heuristic approaches prove inadequate for handling the multi-objective optimization demands of existing cloud infrastructures. This paper presents a comparative analysis of state-of-the-art artificial intelligence and machine learning algorithms for resource allocation. We systematically evaluate 10 algorithms across four categories: Deep Reinforcement Learning approaches, Neural Network architectures, Traditional Machine Learning enhanced methods, and Multi-Agent systems. Analysis of published results demonstrates significant performance improvements across multiple metrics including makespan reduction, cost optimization, and energy efficiency gains compared to traditional methods. The findings reveal that hybrid architectures combining multiple artificial intelligence and machine learning techniques consistently outperform single-method approaches, with edge computing environments showing the highest deployment readiness. Our analysis provides critical insights for both academic researchers and industry practitioners seeking to implement next-generation cloud resource allocation strategies in increasingly complex and dynamic computing environments.
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