Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions (CRDs), Monitor-Analyse-Plan-Execute (MAPE) based control loops, and federated learning (FL) have enabled more proactive and autonomous autoscaling strategies. This paper presents a structured review of these developments. It first introduces a taxonomy of autoscaling techniques based on triggers, targets, prediction models, and evaluation metrics. It then examines predictive autoscaling approaches and CRD-based mechanisms, including Kubernetes operators and reconciliation workflows. Further, it analyses autoscaling in federated learning environments, highlighting reactive and proactive strategies alongside privacy-preserving techniques and container-level isolation. The paper also discusses drift-aware and uncertainty-aware autoscaling, incorporating concepts such as the Autoscaling Drift Index (ADI), feedback-driven correction, and stability control for heterogeneous workloads. Finally, it outlines open challenges and future research directions, providing a foundation for next-generation intelligent predictive autoscaling in cloud-edge environments.

翻译：自动扩缩容是云原生系统中的关键能力，动态工作负载、异构环境及延迟敏感型应用要求高效且自适应的资源管理。基于固定阈值的传统反应式方法往往响应滞后，导致资源失衡、性能退化及扩缩容行为不稳定。近年来，预测模型、Kubernetes自定义资源定义（CRD）、基于监控-分析-规划-执行（MAPE）的控制循环以及联邦学习（FL）等技术的发展，催生了更具前瞻性与自主性的自动扩缩容策略。本文对这些进展进行了结构化综述。首先，基于触发机制、目标对象、预测模型及评估指标构建了自动扩缩容技术的分类体系；继而剖析了预测性自动扩缩容方法及CRD机制（含Kubernetes运算符与协调工作流）；随后，深入分析了联邦学习环境中的自动扩缩容，重点阐释了反应式与主动式策略、隐私保护技术及容器级隔离方案。本文还探讨了漂移感知与不确定性感知的自动扩缩容方法，融合了自动扩缩容漂移指数（ADI）、反馈驱动校正及异构工作负载稳定性控制等概念。最后，系统梳理了开放挑战与未来研究方向，为云边环境下新一代智能预测性自动扩缩容奠定了理论基础。