用AI实现多集群资源主动调度，提升云平台效率与稳定性
AI-Driven Cloud Resource Optimization for Multi-Cluster Environments
- 基于预测学习与反馈机制，跨集群协同优化资源分配
- 实测显示资源效率更高，负载波动时稳定更快，性能更平稳
- 适合需要高可用、低成本的大型分布式云系统团队
现代云原生系统越来越多地采用多集群部署以支持可扩展性、弹性及地理分布。然而，现有资源管理方法仍以被动响应和单集群为中心，难以在动态负载下优化全局行为，导致资源利用率低、响应延迟高、运维开销大。本文提出一种面向多集群云系统的AI驱动自适应资源优化框架。该方法融合预测学习、策略感知决策与持续反馈，通过分析跨集群遥测数据与历史执行模式，动态调整资源分配，在性能、成本与可靠性间取得平衡。原型系统验证表明，相比传统被动方法，本方案显著提升了资源效率，加快了负载波动下的稳定速度，并降低了性能波动。结果表明，智能自适应基础设施管理是构建可扩展、高弹性的云平台的关键。
原文摘要 · Abstract (English)
Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dynamic workloads. These limitations result in inefficient resource utilization, delayed adaptation, and increased operational overhead across distributed environments. This paper presents an AI-driven framework for adaptive resource optimization in multi-cluster cloud systems. The proposed approach integrates predictive learning, policy-aware decision-making, and continuous feedback to enable proactive and coordinated resource management across clusters. By analyzing cross-cluster telemetry and historical execution patterns, the framework dynamically adjusts resource allocation to balance performance, cost, and reliability objectives. A prototype implementation demonstrates improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional reactive approaches. The results highlight the effectiveness of intelligent, self-adaptive infrastructure management as a key enabler for scalable and resilient cloud platforms.
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