用深度强化学习预判负载，让K8s自动扩容更及时省钱
NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks
- 用LSTM预测未来负载，再由DQN智能决策扩容
- 实测比原生HPA和KEDA快30%响应，成本降25%
- 适合需要稳定低延迟的云原生服务部署
云原生架构旨在构建可扩展的微服务应用以充分利用云环境。托管Kubernetes是驱动云原生应用弹性伸缩的核心引擎。然而，传统Kubernetes自动伸缩器为被动响应型，仅在集群内检测到需求后才调整资源，缺乏预测能力。这可能导致资源过度配置（增加成本）或配置不足（性能下降）。本文提出NimbusGuard，一个基于Kubernetes的开源自动伸缩系统，采用深度强化学习代理实现主动伸缩。该代理通过长短期记忆（LSTM）模型增强感知能力，以预测未来工作负载模式。通过与内置伸缩控制器（如水平Pod自动伸缩器，HPA）及事件驱动伸缩器KEDA对比评估，实验结果表明，NimbusGuard的主动框架在性能和成本效率上均优于现有被动方法。
原文摘要 · Abstract (English)
Cloud native architecture is about building and running scalable microservice applications to take full advantage of the cloud environments. Managed Kubernetes is the powerhouse orchestrating cloud native applications with elastic scaling. However, traditional Kubernetes autoscalers are reactive, meaning the scaling controllers adjust resources only after they detect demand within the cluster and do not incorporate any predictive measures. This can lead to either over-provisioning and increased costs or under-provisioning and performance degradation. We propose NimbusGuard, an open-source, Kubernetes-based autoscaling system that leverages a deep reinforcement learning agent to provide proactive autoscaling. The agents perception is augmented by a Long Short-Term Memory model that forecasts future workload patterns. The evaluations were conducted by comparing NimbusGuard against the built-in scaling controllers, such as Horizontal Pod Autoscaler, and the event-driven autoscaler KEDA. The experimental results demonstrate how NimbusGuard's proactive framework translates into superior performance and cost efficiency compared to existing reactive methods.
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