Adaptable TeaStore

TL;DR

Adaptable TeaStore is a flexible cloud-native architecture extension that supports dynamic reconfiguration, resilience, and adaptation scenarios, enabling systematic evaluation of self-adaptive mechanisms in realistic cloud environments.

Contribution

It introduces Adaptable TeaStore, an extension of TeaStore with explicit adaptation support, multiple component versions, external outsourcing, and cache mechanisms for research on self-adaptive cloud architectures.

Findings

Provides a comprehensive catalogue of adaptation scenarios.

Includes an open-source implementation with APIs for experiments.

Enables systematic evaluation of adaptation strategies in cloud environments.

Abstract

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.