Architectural Runtime Modeling and Visualization for Quality-Aware DevOps in Cloud Applications

Heinrich, Robert; Zirkelbach, Christian; Jung, Reiner

doi:10.1109/icsaw.2017.33

Cited by 7 publications

(11 citation statements)

References 2 publications

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“…An engineering model typically starts by being descriptive (e.g., a domain model describing key concepts and relationships), and then at design time is refined/transformed into a prescriptive model. But once the system is built as prescribed, the model becomes descriptive again as a form of documentation [8]. An engineering model can also be used as a predictive model: e.g., an architecture model could be used to predict the performance of a specific configuration.…”

Section: B Complementarities and Synergiesmentioning

confidence: 99%

A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

et al. 2021

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Section: B Complementarities and Synergiesmentioning

confidence: 99%

A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

et al. 2021

Self Cite

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“…228,[255][256][257][258][259][260][261][262][263] Other contributions are catered for systems which exhibit a similar level of dynamism to microservices. [264][265][266] However, these contributions would need to be leveraged with objective reasoning that considers both the added value and cost of granularity adaptation.…”

Section: Fully Autonomous Contributionsmentioning

confidence: 99%

“…Several contributions in this field manifest runtime changes to the architecture 228,255‐263 . Other contributions are catered for systems which exhibit a similar level of dynamism to microservices 264‐266 . However, these contributions would need to be leveraged with objective reasoning that considers both the added value and cost of granularity adaptation.…”

Section: Relevant Research Fieldsmentioning

confidence: 99%

Microservice transition and its granularity problem: A systematic mapping study

Hassan¹,

Bahsoon²,

Kazman

2020

Softw Pract Exp

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Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying that transition. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and-practice related to reasoning about microservice granularity; it reviews modeling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.

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“…iObserve [21] aims to increase the human comprehensibility of run-time changes by updating an architecture model accordingly. The authors defined specific monitoring records using IRL and map the resulting measurements to the corresponding parts in the PCM using the so-called Run-time Architecture Correspondence Meta-Model (RAC) [20].…”

Section: Iobservementioning

confidence: 99%

Incremental Calibration of Architectural Performance Models with Parametric Dependencies

Mazkatli

Monschein

Grohmann

et al. 2020

2020 IEEE International Conference on Software Architecture (ICSA)

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Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary.Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating.In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly.For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.

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Architectural Runtime Modeling and Visualization for Quality-Aware DevOps in Cloud Applications

Cited by 7 publications

References 2 publications

A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

Microservice transition and its granularity problem: A systematic mapping study

Incremental Calibration of Architectural Performance Models with Parametric Dependencies

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