自动扩缩容的关键考量：来自基准微服务的经验教训
Key Considerations for Auto-Scaling: Lessons from Benchmark Microservices
AI总结：
本文针对微服务自动扩缩容现有基准忽略基础设计实践的问题，结合前沿方法与常用基准，按软件生命周期三阶段梳理关键考量，经Sock-Shop基准验证，解决这些问题可提升扩缩容稳定性与效率。
摘要：
微服务已成为构建可扩展、模块化云原生系统的主流架构范式。然而，在这类系统中实现有效的自动扩缩容仍是一项艰巨挑战，因为它不仅依赖先进的扩缩容技术，还依赖合理的设计、实现与部署实践。但现有基准测试往往忽略这些基础层面，导致难以在真实条件下评估自动扩缩容方法。\n本文通过将多种前沿自动扩缩容方法应用于广泛使用的微服务基准，识别出一组实用的自动扩缩容考量因素。为梳理这些发现，我们根据问题在软件生命周期中出现的阶段将其分类：架构阶段、实现阶段与部署阶段。架构阶段涵盖服务拆分、服务间依赖等高层决策；实现阶段包含初始化开销、指标埋点、错误传播等方面；部署阶段聚焦资源限制、健康检查等运行时配置。\n我们使用Sock-Shop基准验证了这些考量因素，并评估了多种自动扩缩容策略，包括基于阈值的方法、控制论方法、基于学习的方法、黑盒优化方法以及依赖感知方法。研究结果表明，忽略关键的生命周期相关问题会降低自动扩缩容器的性能，而解决这些问题则能实现更稳定、更高效的扩缩容。这些结果凸显了具备生命周期意识的工程实践对于充分释放微服务系统中自动扩缩容潜力的重要性。
英文摘要：
Microservices have become the dominant architectural paradigm for building scalable and modular cloud-native systems. However, achieving effective auto-scaling in such systems remains a non-trivial challenge, as it depends not only on advanced scaling techniques but also on sound design, implementation, and deployment practices. Yet, these foundational aspects are often overlooked in existing benchmarks, making it difficult to evaluate autoscaling methods under realistic conditions. In this paper, we identify a set of practical auto-scaling considerations by applying several state-of-the-art autoscaling methods to widely used microservice benchmarks. To structure these findings, we classify the issues based on when they arise during the software lifecycle: Architecture, Implementation, and Deployment. The Architecture phase covers high-level decisions such as service decomposition and inter-service dependencies. The Implementation phase includes aspects like initialization overhead, metrics instrumentation, and error propagation. The Deployment phase focuses on runtime configurations such as resource limits and health checks. We validate these considerations using the Sock-Shop benchmark and evaluate diverse auto-scaling strategies, including threshold-based, control-theoretic, learning-based, black-box optimization, and dependency-aware approaches. Our findings show that overlooking key lifecycle concerns can degrade autoscaler performance, while addressing them leads to more stable and efficient scaling. These results underscore the importance of lifecycle-aware engineering for unlocking the full potential of auto-scaling in microservice-based systems.
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