Microservice-based cloud applications face changing workloads, evolving request paths, variable network conditions, interference, and failures. These dynamics couple autoscaling, placement, routing, isolation, and remediation. The survey examines dynamics-aware adaptive management for microservices. Its taxonomy covers control locus, modeled dynamics, adaptation strategy, and evaluation evidence; objectives and telemetry are cross-cutting. A synthesis of 84 system entries and 13 evaluation artifacts shows that production dynamics are often partially modeled. Reported gains also depend on evaluation fidelity. Key future directions include cross-layer coordination, telemetry-to-control abstractions, safe learning-based control, and reproducible dynamic evaluation.

翻译：基于微服务的云应用面临工作负载变化、请求路径演进、网络条件波动、干扰及故障等动态挑战。这些动态因素将自动扩缩容、服务部署、路由调度、故障隔离与修复等机制紧密耦合。本文对面向微服务的动态感知自适应管理技术进行系统性综述，其分类体系涵盖控制主体、动态建模方法、自适应策略及评估验证四个维度，同时将优化目标与遥测技术作为贯穿性要素。通过对84个系统案例与13项评估工件的综合分析表明，生产环境中的动态特征往往仅被部分建模，且报告的性能提升高度依赖于评估保真度。未来关键方向包括跨层协同优化、遥测-控制抽象机制、基于安全学习的控制方法以及可复现的动态评估框架。