Modern cloud-native systems require adapting dynamically to changing operational conditions, including service outages, traffic surges, and evolving user requirements. While existing benchmarks provide valuable testbeds for performance and scalability evaluation, they lack explicit support for studying adaptation mechanisms, reconfiguration strategies, and graceful degradation. These limitations hinder systematic research on self-adaptive architectures in realistic cloud environments. To cover this gap, we introduce Adaptable TeaStore, an extension of the renowned TeaStore architecture that incorporates adaptability as a first-class design concern. Our extension distinguishes between mandatory and optional services, supports multiple component versions -- with varying resource requirements and functionality levels -- considers the outsourcing of functionalities to external providers, and provides local cache mechanisms for performance and resilience. These features enable the systematic exploration of reconfiguration policies across diverse operational scenarios. We discuss a broad catalogue of reference adaptation scenarios centred around Adaptable TeaStore, useful to evaluate the ability of a given adaptation technology to address conditions such as component unavailability, cyberattacks, provider outages, benign/malicious traffic increases, and user-triggered reconfigurations. Moreover, we present an open-source implementation of the architecture with APIs for metrics collection and adaptation triggers, to enable reproducible experiments.

翻译：现代云原生系统需要动态适应不断变化的运行条件，包括服务中断、流量激增和用户需求的演变。虽然现有基准测试为性能和可扩展性评估提供了宝贵的测试平台，但它们缺乏对适应性机制、重配置策略和优雅降级研究的明确支持。这些限制阻碍了在真实云环境中对自适应架构的系统性研究。为填补这一空白，我们引入了可适应性茶店系统——这是对著名茶店架构的扩展，将适应性作为首要设计考量。我们的扩展区分了必需服务和可选服务，支持具有不同资源需求和功能级别的多组件版本，考虑了将功能外包给外部供应商，并为性能和弹性提供了本地缓存机制。这些特性使得能够系统性地探索不同运行场景下的重配置策略。我们围绕可适应性茶店系统讨论了一系列广泛的参考适应场景，可用于评估特定适应技术在应对组件不可用、网络攻击、供应商中断、良性/恶意流量增加以及用户触发的重配置等条件时的能力。此外，我们提供了该架构的开源实现，包含用于指标收集和适应触发的API，以实现可重复的实验。