标题： 用于弹性云资源扩展的协作多智能体强化学习方法 显示英文标题

标题： Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling

摘要： 本文解决了云计算环境中快速资源变化和高度不确定的任务负载的挑战。 它提出了一种基于多智能体系统的弹性云资源扩展优化方法。 该方法部署多个自主智能体并行感知资源状态并做出局部决策。 在保持系统分布式特性的同时，引入了协作价值函数以实现全局协调。 这提高了资源调度的响应能力并增强了整体系统性能。 为了加强系统预见性，设计了一个轻量级状态预测模型。 它帮助智能体识别未来工作负载趋势并优化扩展动作的选择。 在策略训练方面，该方法采用集中训练和分散执行的强化学习框架。 这使得智能体能够在信息不完整的情况下有效学习并协调策略。 本文还构建了典型的云场景，包括多租户和突发流量，以评估所提出的方法。 评估重点在于资源隔离、服务质量保障和鲁棒性。 实验结果表明，所提出的多智能体扩展策略在资源利用率、SLA违反控制和调度延迟方面优于现有方法。 结果展示了强大的适应性和智能调节能力。 这为解决复杂云平台中的弹性资源扩展问题提供了一种高效可靠的新方法。 显示英文摘要

摘要： This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method deploys multiple autonomous agents to perceive resource states in parallel and make local decisions. While maintaining the distributed nature of the system, it introduces a collaborative value function to achieve global coordination. This improves the responsiveness of resource scheduling and enhances overall system performance. To strengthen system foresight, a lightweight state prediction model is designed. It assists agents in identifying future workload trends and optimizes the selection of scaling actions. For policy training, the method adopts a centralized training and decentralized execution reinforcement learning framework. This enables agents to learn effectively and coordinate strategies under conditions of incomplete information. The paper also constructs typical cloud scenarios, including multi-tenancy and burst traffic, to evaluate the proposed method. The evaluation focuses on resource isolation, service quality assurance, and robustness. Experimental results show that the proposed multi-agent scaling strategy outperforms existing methods in resource utilization, SLA violation control, and scheduling latency. The results demonstrate strong adaptability and intelligent regulation. This provides an efficient and reliable new approach to solving the problem of elastic resource scaling in complex cloud platforms.