The Adaptable TeaStore specification provides a microservice-based case study for implementing self-adaptation through a control loop. We argue that implementations of this specification should be informed by key properties of self-adaptation: system-wide consistency (coordinated adaptations across replicas), planning (executing an adaptation until appropriate conditions are met), and modularity (clean integration of adaptation logic). In this implementation discussion paper, we examine how software architectural methods, the cloud-native Operator pattern, and legacy programming language techniques can decouple self-adaptive control logic from the TeaStore application. We analyze the trade-offs that these different approaches make between fine-grained expressive adaptation and system-wide control, and highlight when reuse of adaptation strategies is most effective. Our analysis suggests that these approaches are not mutually exclusive but can be combined into a multi-tiered architecture for self-adaptive microservices.
When designing new web applications, developers must cope with different kinds of constraints relative to the resources they rely on: software, hardware, network, online micro-services, or any combination of the mentioned entities. Together, these entities form a complex system of communicating interdependent processes, physical or logical. It is very desirable that such system ensures its robustness to provide a good quality of service. In this paper we introduce Chips, a language that aims at facilitating the design of models made of various entwined components. It allows the description of applications in the form of functional blocks. Chips mixes notions from control theory and general purpose programming languages to generate robust component-based models. This paper presents how to use Chips to systematically design, model and analyse a complex system project, using a variation of the Adaptable TeaStore application as running example.
[Context and Motivation] Global energy consumption has been steadily increasing in recent years, with data centers emerging as major contributors. This growth is largely driven by the widespread migration of applications to the Cloud, alongside a rising number of users consuming digital content. Dynamic adaptation (or self-adaptive) approaches appear as a way to reduce, at runtime and under certain constraints, the energy consumption of software applications. [Question/Problem] Despite efforts to make energy-efficiency a primary goal in the dynamic adaptation of software applications, there is still a gap in understanding how to equip these self-adaptive software systems (SAS), which are dynamically adapted at runtime, with effective energy consumption monitoring tools that enable energy-awareness. Furthermore, the extent to which such an energy consumption monitoring tool impacts the overall energy consumption of the SAS ecosystem has not yet been thoroughly explored. [Methodology] To address this gap, we introduce the EnCoMSAS (Energy Consumption Monitoring for Self-Adaptive Systems) tool that allows to gather the energy consumed by distributed software applications deployed, for instance, in the Cloud. EnCoMSAS enables the evaluation of energy consumption of SAS variants at runtime. It allows to integrate energy-efficiency as a main goal in the analysis and execution of new adaptation plans for the SAS. In order to evaluate the effectiveness of EnCoMSAS and investigate its impact on the overall energy consumption of the SAS ecosystem, we conduct an empirical study by using the Adaptable TeaStore case study. Adaptable TeaStore is a self-adaptive extension of the TeaStore application, a microservice benchmarking application. For this study, we focus on the recommender service of Adaptable TeaStore. Regarding the experiments, we first equip Adaptable TeaStore with EnCoMSAS. Next, we execute Adaptable TeaStore by varying workload conditions that simulate users interactions. Finally, we use EnCoMSAS for gathering and assessing the energy consumption of the recommender algorithms of Adaptable TeaStore. To run these experiments, we use nodes of the Grid5000 testbed. [Results] The results show that EnCoMSAS is effective in collecting energy consumption of software applications for enabling dynamic adaptation at runtime. The observed correlation between CPU usage and energy consumption collected by EnCoMSAS provides evidence supporting the validity of the collected energy measurements. Moreover, we point out, through EnCoMSAS, that energy consumption is influenced not only by the algorithmic complexity but also by the characteristics of the deployment environment. Finally, the results show that the impact of EnCoMSAS on the overall energy consumption of the SAS ecosystem is comparatively modest with respect to the entire set of the TeaStore applications microservices.
Modern cloud architectures demand self-adaptive capabilities to manage dynamic operational conditions. Yet, existing solutions often impose centralized control models ill-suited to microservices decentralized nature. This paper presents AdaptiFlow, a framework that leverages well-established principles of autonomous computing to provide abstraction layers focused on the Monitor and Execute phases of the MAPE-K loop. By decoupling metrics collection and action execution from adaptation logic, AdaptiFlow enables microservices to evolve into autonomous elements through standardized interfaces, preserving their architectural independence while enabling system-wide adaptability. The framework introduces: (1) Metrics Collectors for unified infrastructure/business metric gathering, (2) Adaptation Actions as declarative actuators for runtime adjustments, and (3) a lightweight Event-Driven and rule-based mechanism for adaptation logic specification. Validation through the enhanced Adaptable TeaStore benchmark demonstrates practical implementation of three adaptation scenarios targeting three levels of autonomy self-healing (database recovery), self-protection (DDoS mitigation), and self-optimization (traffic management) with minimal code modification per service. Key innovations include a workflow for service instrumentation and evidence that decentralized adaptation can emerge from localized decisions without global coordination. The work bridges autonomic computing theory with cloud-native practice, providing both a conceptual framework and concrete tools for building resilient distributed systems. Future work includes integration with formal coordination models and application of adaptation techniques relying on AI agents for proactive adaptation to address complex adaptation scenarios.
This paper presents how an existing framework for offline performance optimization can be applied to microservice applications during the Release phase of the DevOps life cycle. Optimization of resource allocation configuration parameters for CPU and memory during the Release phase remains a largely unexplored problem as most research has focused on intelligent scheduling and autoscaling of microservices during the Ops stage of the DevOps cycle. Yet horizontal auto-scaling of containers, based on CPU usage for instance, may still leave these containers with an inappropriately allocated amount of memory, if no upfront fine-tuning of both resources is applied before the Deployment phase. We evaluate the performance optimization framework using the TeaStore microservice application and statistically compare different optimization algorithms, supporting informed decisions about their trade-offs between sampling cost and distance to the optimal resource configuration. This shows that upfront factor screening, for reducing the search space, is helpful when the goal is to find the optimal resource configuration with an affordable sampling budget. When the goal is to statistically compare different algorithms, screening must also be applied to make data collection of all data points in the search space feasible. If the goal is to find a near-optimal configuration, however, it is better to run bayesian optimization without screening.
