DML

Update: This tool is no longer maintained and supported.

Descartes Modeling Language

The Descartes Modeling Language (DML) is an architecture-level modeling language for quality-of-service and resource management of modern dynamic IT systems and infrastructures. DML is designed to serve as a basis for self-aware systems management during operation, ensuring that system quality-of-service requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible. The term quality-of-service (QoS) is used to refer to performance (response time, throughput, scalability and efficiency) and dependability (availability, reliability and security). The current version of DML is focused on performance and availability, however, the modeling language itself is designed in a generic fashion and it is intended to eventually support further QoS properties.

More information can be found on the following pages:

If you have any questions, please contact Simon Eismann or Johannes Grohmann.

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Publications

2019[ to top ]

Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques. J. Grohmann; S. Eismann; S. Elflein; M. Mazkatli; J. von Kistowski; S. Kounev; in 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2019). 309–322.
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Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard.

@inproceedings{GrEiElMaKiKo2019-MASCOTS-DependencyIdentification, abstract = {Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard.}, author = {Grohmann, Johannes and Eismann, Simon and Elflein, Sven and Mazkatli, Manar and von Kistowski, J{\'o}akim and Kounev, Samuel}, booktitle = {2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)}, keywords = {Automated_model_learning}, month = 10, note = {{Acceptance Rate: 23.8% (29/122)}}, pages = {309–322}, publisher = {IEEE Computer Society}, series = {MASCOTS '19}, title = {{Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques}}, year = 2019 }

%0 Conference Paper %1 GrEiElMaKiKo2019-MASCOTS-DependencyIdentification %A Grohmann, Johannes %A Eismann, Simon %A Elflein, Sven %A Mazkatli, Manar %A von Kistowski, Jóakim %A Kounev, Samuel %B 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) %D 2019 %I IEEE Computer Society %P 309--322 %R 10.1109/MASCOTS.2019.00042 %T Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques %U https://doi.org/10.1109/MASCOTS.2019.00042 %X Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard. %@ 978-1-7281-4950-9
Automation in Software Performance Engineering Based on a Declarative Specification of Concerns. J. C. Walter; Thesis; Universität Würzburg. (2019).
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@phdthesis{Walter2018-phd, author = {Walter, J{\"u}rgen Christian}, keywords = {PMX}, school = {Universit{\"a}t W{\"u}rzburg}, title = {Automation in Software Performance Engineering Based on a Declarative Specification of Concerns}, type = {doctoralthesis}, year = 2019 }

%0 Thesis %1 Walter2018-phd %A Walter, Jürgen Christian %D 2019 %T Automation in Software Performance Engineering Based on a Declarative Specification of Concerns %U https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/18090
Online model learning for self-aware computing infrastructures. S. Spinner; J. Grohmann; S. Eismann; S. Kounev; in Journal of Systems and Software (2019). 147 1–16.
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Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.

@article{SpGrEiKo2019-JSS-ModelLearning, abstract = {Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.}, author = {Spinner, Simon and Grohmann, Johannes and Eismann, Simon and Kounev, Samuel}, journal = {Journal of Systems and Software}, keywords = {Virtualization}, pages = {1–16}, title = {Online model learning for self-aware computing infrastructures}, volume = 147, year = 2019 }

%0 Journal Article %1 SpGrEiKo2019-JSS-ModelLearning %A Spinner, Simon %A Grohmann, Johannes %A Eismann, Simon %A Kounev, Samuel %D 2019 %J Journal of Systems and Software %P 1--16 %R 10.1016/j.jss.2018.09.089 %T Online model learning for self-aware computing infrastructures %U https://doi.org/10.1016/j.jss.2018.09.089 %V 147 %X Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.
Integrating Statistical Response Time Models in Architectural Performance Models. S. Eismann; J. Grohmann; J. Walter; J. von Kistowski; S. Kounev; in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71–80.
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Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.

@inproceedings{EiGrWaKiKo2019-ICSA-Integrating, abstract = {Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.}, author = {Eismann, Simon and Grohmann, Johannes and Walter, J{\"u}rgen and von Kistowski, J{\'o}akim and Kounev, Samuel}, booktitle = {Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA)}, keywords = {PRISMA}, month = {03}, note = {Acceptance Rate: 21,9\% (21/96)}, pages = {71–80}, publisher = {IEEE}, title = {{Integrating Statistical Response Time Models in Architectural Performance Models}}, year = 2019 }

%0 Conference Paper %1 EiGrWaKiKo2019-ICSA-Integrating %A Eismann, Simon %A Grohmann, Johannes %A Walter, Jürgen %A von Kistowski, Jóakim %A Kounev, Samuel %B Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) %D 2019 %I IEEE %P 71--80 %R 10.1109/ICSA.2019.00016 %T Integrating Statistical Response Time Models in Architectural Performance Models %U https://doi.org/10.1109/ICSA.2019.00016 %X Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.

2018[ to top ]

Flexible Performance Predictions at Run-time. S. Eismann; (2018, April).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Performance modeling is a powerful tool to predict the performance of a software system, especially for complex software systems deployed in cloud enviroments. At run-time, these predictions can be used for a variety of purposes. A performance engineer can analyze them to find potential bottlenecks or investigate solutions to an existing performance issue. Alternatively, a design space exploration tool can utilize performance predictions to find an optimal system configuration for a given scenario. A relatively novel use case for performance predictions is proactive auto-scaling. Proactive auto-scaling uses load forecasts to predict load spikes and proactively adapt the system accordingly. Here performance predictions are required to find a suitable system configuration for the incoming load spike. However, all of these use cases have different requirements for the performance predictions in terms of time-to-result, accuracy, and the required metrics. For example during design space exploration accurate predictions of both utilization and response time are required, without major time limitations. An auto-scaler, on the other hand, requires only rough utilization predictions, but with strict time-to-result requirements. A performance engineer requires a balance of accuracy and time-to-result. He is however often only interested in specific metrics, not the performance of the whole system. While a vast number of performance prediction approaches has been proposed, there is as of yet no approach flexible enough to fit all use cases. In this talk, we present our vision for flexible performance predictions at run-time. We envision the usage of one model describing the architecture and performance relevant properties of the system. This model can then be transformed to a number of solution formalisms, such as Queuing Networks (QNs), Layered Queuing Networks (LQNs) or stochastic process models. For these solution formalisms, a series of solution approaches exist, ranging from simple approximations, analytical approaches to full-scale simulations. This ensures that for each use case a suitable solution approach is available without having to maintain multiple models of the system. However, manual selection requires expert knowledge and manual intervention. Therefore, we aim to design an intelligent selection algorithm. This algorithm selects a suitable solution approach for every performance query. We define a performance query as a collection of requested performance metrics, a lower limit for the prediction accuracy and an upper limit for the time-to-result. In order to select a suitable solution approach for a query we first filter out all approaches that are not capable of predicting the requested performance metrics. For example, many analytical approaches are not capable of predicting response time distributions. In a next step, the selection algorithm needs to predict the accuracy and time-to-result for every solution approach. To predict the accuracy we plan to combine expert knowledge about the solvers with smart transformations, that can estimate how well the initial model is represented in the transformation output. For each simplification a transformation makes a number of penalty points, depending the level of simplification is awarded. For example, if a transformation represents loops in a probabilistic fashion only two penalty points per loop would be awarded, while a transformation that ignores passive resources would award a larger number of penalty points for each passive resource in the original model. Additional penalty points are awarded in case the applied solution approach calculates approximations instead of exact results. These penalty scores are determined once using expert knowledge. The resulting penalty score represents an indicator for the expected accuracy of a solution approach. We plan to utilize machine learning approaches to estimate the time-to-result. In general, estimating the time-to-result for performance predictions is challenging. However, we do not need to predict the time-to-result for a previously unseen model. After the inception of the performance model, each solution approach can be benchmarked to generate training data. From this training data, the machine learning approaches can then estimate the time-to-result for a specific request. The accuracy and time-to-result estimations will enable the design of an automated selection algorithm, that selects a suitable solution approach for each performance query. This allows for flexible performance predictions using a single model.

@misc{talk-Eismann-HotCloudPerf2018, abstract = {Performance modeling is a powerful tool to predict the performance of a software system, especially for complex software systems deployed in cloud enviroments. At run-time, these predictions can be used for a variety of purposes. A performance engineer can analyze them to find potential bottlenecks or investigate solutions to an existing performance issue. Alternatively, a design space exploration tool can utilize performance predictions to find an optimal system configuration for a given scenario. A relatively novel use case for performance predictions is proactive auto-scaling. Proactive auto-scaling uses load forecasts to predict load spikes and proactively adapt the system accordingly. Here performance predictions are required to find a suitable system configuration for the incoming load spike. However, all of these use cases have different requirements for the performance predictions in terms of time-to-result, accuracy, and the required metrics. For example during design space exploration accurate predictions of both utilization and response time are required, without major time limitations. An auto-scaler, on the other hand, requires only rough utilization predictions, but with strict time-to-result requirements. A performance engineer requires a balance of accuracy and time-to-result. He is however often only interested in specific metrics, not the performance of the whole system. While a vast number of performance prediction approaches has been proposed, there is as of yet no approach flexible enough to fit all use cases. In this talk, we present our vision for flexible performance predictions at run-time. We envision the usage of one model describing the architecture and performance relevant properties of the system. This model can then be transformed to a number of solution formalisms, such as Queuing Networks (QNs), Layered Queuing Networks (LQNs) or stochastic process models. For these solution formalisms, a series of solution approaches exist, ranging from simple approximations, analytical approaches to full-scale simulations. This ensures that for each use case a suitable solution approach is available without having to maintain multiple models of the system. However, manual selection requires expert knowledge and manual intervention. Therefore, we aim to design an intelligent selection algorithm. This algorithm selects a suitable solution approach for every performance query. We define a performance query as a collection of requested performance metrics, a lower limit for the prediction accuracy and an upper limit for the time-to-result. In order to select a suitable solution approach for a query we first filter out all approaches that are not capable of predicting the requested performance metrics. For example, many analytical approaches are not capable of predicting response time distributions. In a next step, the selection algorithm needs to predict the accuracy and time-to-result for every solution approach. To predict the accuracy we plan to combine expert knowledge about the solvers with smart transformations, that can estimate how well the initial model is represented in the transformation output. For each simplification a transformation makes a number of penalty points, depending the level of simplification is awarded. For example, if a transformation represents loops in a probabilistic fashion only two penalty points per loop would be awarded, while a transformation that ignores passive resources would award a larger number of penalty points for each passive resource in the original model. Additional penalty points are awarded in case the applied solution approach calculates approximations instead of exact results. These penalty scores are determined once using expert knowledge. The resulting penalty score represents an indicator for the expected accuracy of a solution approach. We plan to utilize machine learning approaches to estimate the time-to-result. In general, estimating the time-to-result for performance predictions is challenging. However, we do not need to predict the time-to-result for a previously unseen model. After the inception of the performance model, each solution approach can be benchmarked to generate training data. From this training data, the machine learning approaches can then estimate the time-to-result for a specific request. The accuracy and time-to-result estimations will enable the design of an automated selection algorithm, that selects a suitable solution approach for each performance query. This allows for flexible performance predictions using a single model.}, author = {Eismann, Simon}, howpublished = {HotCloudperf 2018}, keywords = {Meta-models}, month = {04}, title = {{Flexible Performance Predictions at Run-time}}, year = 2018 }

%0 Generic %1 talk-Eismann-HotCloudPerf2018 %A Eismann, Simon %D 2018 %T Flexible Performance Predictions at Run-time %X Performance modeling is a powerful tool to predict the performance of a software system, especially for complex software systems deployed in cloud enviroments. At run-time, these predictions can be used for a variety of purposes. A performance engineer can analyze them to find potential bottlenecks or investigate solutions to an existing performance issue. Alternatively, a design space exploration tool can utilize performance predictions to find an optimal system configuration for a given scenario. A relatively novel use case for performance predictions is proactive auto-scaling. Proactive auto-scaling uses load forecasts to predict load spikes and proactively adapt the system accordingly. Here performance predictions are required to find a suitable system configuration for the incoming load spike. However, all of these use cases have different requirements for the performance predictions in terms of time-to-result, accuracy, and the required metrics. For example during design space exploration accurate predictions of both utilization and response time are required, without major time limitations. An auto-scaler, on the other hand, requires only rough utilization predictions, but with strict time-to-result requirements. A performance engineer requires a balance of accuracy and time-to-result. He is however often only interested in specific metrics, not the performance of the whole system. While a vast number of performance prediction approaches has been proposed, there is as of yet no approach flexible enough to fit all use cases. In this talk, we present our vision for flexible performance predictions at run-time. We envision the usage of one model describing the architecture and performance relevant properties of the system. This model can then be transformed to a number of solution formalisms, such as Queuing Networks (QNs), Layered Queuing Networks (LQNs) or stochastic process models. For these solution formalisms, a series of solution approaches exist, ranging from simple approximations, analytical approaches to full-scale simulations. This ensures that for each use case a suitable solution approach is available without having to maintain multiple models of the system. However, manual selection requires expert knowledge and manual intervention. Therefore, we aim to design an intelligent selection algorithm. This algorithm selects a suitable solution approach for every performance query. We define a performance query as a collection of requested performance metrics, a lower limit for the prediction accuracy and an upper limit for the time-to-result. In order to select a suitable solution approach for a query we first filter out all approaches that are not capable of predicting the requested performance metrics. For example, many analytical approaches are not capable of predicting response time distributions. In a next step, the selection algorithm needs to predict the accuracy and time-to-result for every solution approach. To predict the accuracy we plan to combine expert knowledge about the solvers with smart transformations, that can estimate how well the initial model is represented in the transformation output. For each simplification a transformation makes a number of penalty points, depending the level of simplification is awarded. For example, if a transformation represents loops in a probabilistic fashion only two penalty points per loop would be awarded, while a transformation that ignores passive resources would award a larger number of penalty points for each passive resource in the original model. Additional penalty points are awarded in case the applied solution approach calculates approximations instead of exact results. These penalty scores are determined once using expert knowledge. The resulting penalty score represents an indicator for the expected accuracy of a solution approach. We plan to utilize machine learning approaches to estimate the time-to-result. In general, estimating the time-to-result for performance predictions is challenging. However, we do not need to predict the time-to-result for a previously unseen model. After the inception of the performance model, each solution approach can be benchmarked to generate training data. From this training data, the machine learning approaches can then estimate the time-to-result for a specific request. The accuracy and time-to-result estimations will enable the design of an automated selection algorithm, that selects a suitable solution approach for each performance query. This allows for flexible performance predictions using a single model.
Tools for Declarative Performance Engineering. J. Walter; S. Eismann; J. Grohmann; D. Okanovic; S. Kounev; in Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 53–56.
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Performance is of particular relevance to software system design, operation, and evolution. However, the application of performance engineering approaches to solve a given user concern is challenging and requires expert knowledge. In this tutorial paper, we guide the reader step-by-step through the answering of performance concerns following the idea of declarative performance engineering. We explain tools available online, which can be used for automating huge parts of the software performance engineering process. In particular, we present a performance concern language, for which we provide automated answering and visualization referring to measurement-based and model-based analysis. We also detail how to derive performance models using automated extraction of architectural performance models and modeling of parametric dependencies.

@inproceedings{WaEiGrOkKo2018-ICPE-Tools-for-DPE-Tutorial, abstract = {Performance is of particular relevance to software system design, operation, and evolution. However, the application of performance engineering approaches to solve a given user concern is challenging and requires expert knowledge. In this tutorial paper, we guide the reader step-by-step through the answering of performance concerns following the idea of declarative performance engineering. We explain tools available online, which can be used for automating huge parts of the software performance engineering process. In particular, we present a performance concern language, for which we provide automated answering and visualization referring to measurement-based and model-based analysis. We also detail how to derive performance models using automated extraction of architectural performance models and modeling of parametric dependencies.}, address = {New York, NY, USA}, author = {Walter, J{\"u}rgen and Eismann, Simon and Grohmann, Johannes and Okanovic, Dusan and Kounev, Samuel}, booktitle = {Companion of the 2018 ACM/SPEC International Conference on Performance Engineering}, keywords = {DQL}, pages = {53–56}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '18}, title = {{Tools for Declarative Performance Engineering}}, year = 2018 }

%0 Conference Paper %1 WaEiGrOkKo2018-ICPE-Tools-for-DPE-Tutorial %A Walter, Jürgen %A Eismann, Simon %A Grohmann, Johannes %A Okanovic, Dusan %A Kounev, Samuel %B Companion of the 2018 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2018 %I Association for Computing Machinery (ACM) %P 53--56 %R 10.1145/3185768.3185777 %T Tools for Declarative Performance Engineering %U https://doi.org/10.1145/3185768.3185777 %X Performance is of particular relevance to software system design, operation, and evolution. However, the application of performance engineering approaches to solve a given user concern is challenging and requires expert knowledge. In this tutorial paper, we guide the reader step-by-step through the answering of performance concerns following the idea of declarative performance engineering. We explain tools available online, which can be used for automating huge parts of the software performance engineering process. In particular, we present a performance concern language, for which we provide automated answering and visualization referring to measurement-based and model-based analysis. We also detail how to derive performance models using automated extraction of architectural performance models and modeling of parametric dependencies. %@ 9781450356299
The Vision of Self-aware Reordering of Security Network Function Chains. L. Iffländer; J. Walter; S. Eismann; S. Kounev; in Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 1–4.
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Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.

@inproceedings{IfWaEiKo2018-ICPE-SSFC-Vision, abstract = {Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.}, address = {New York, NY, USA}, author = {Iffländer, Lukas and Walter, Jürgen and Eismann, Simon and Kounev, Samuel}, booktitle = {Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering}, keywords = {Self-adaptive-systems}, pages = {1–4}, publisher = {ACM}, series = {ICPE '18}, title = {The Vision of Self-aware Reordering of Security Network Function Chains}, year = 2018 }

%0 Conference Paper %1 IfWaEiKo2018-ICPE-SSFC-Vision %A Iffländer, Lukas %A Walter, Jürgen %A Eismann, Simon %A Kounev, Samuel %B Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2018 %I ACM %P 1--4 %T The Vision of Self-aware Reordering of Security Network Function Chains %U https://dl.acm.org/doi/10.1145/3185768.3186309 %X Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.
Modeling of Parametric Dependencies for Performance Prediction of Component-based Software Systems at Run-time. S. Eismann; J. Walter; J. von Kistowski; S. Kounev; in 2018 IEEE International Conference on Software Architecture (ICSA) (2018). 135–144.
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- [ EndNote ]
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Model-based performance analysis can be leveraged to explore performance properties of software systems. To capture the behavior of varying workload mixes, configurations, and deployments of a software system requires formal modeling of the impact of configuration parameters and user input on the system behavior. Such influences are represented as parametric dependencies in software performance models. Existing modeling approaches focus on modeling parametric dependencies at design-time. This paper identifies runtime specific parametric dependency features, which are not supported by existing work. Therefore, this paper proposes a novel modeling methodology for parametric dependencies and a corresponding graph-based resolution algorithm. This algorithm enables the solution of models containing component instance-level dependencies, variables with multiple descriptions in parallel, and correlations modeled as parametric dependencies. We integrate our work into the Descartes Modeling Language (DML), allowing for accurate and efficient modeling and analysis of parametric dependencies. These performance predictions are valuable for various purposes such as capacity planning, bottleneck analysis, configuration optimization and proactive auto-scaling. Our evaluation analyzes a video store application. The prediction for varying language mixes and video sizes shows a mean error below 5% for utilization and below 10% for response time.

@inproceedings{EiWaKiKo2018-ParametricDependencies, abstract = {Model-based performance analysis can be leveraged to explore performance properties of software systems. To capture the behavior of varying workload mixes, configurations, and deployments of a software system requires formal modeling of the impact of configuration parameters and user input on the system behavior. Such influences are represented as parametric dependencies in software performance models. Existing modeling approaches focus on modeling parametric dependencies at design-time. This paper identifies runtime specific parametric dependency features, which are not supported by existing work. Therefore, this paper proposes a novel modeling methodology for parametric dependencies and a corresponding graph-based resolution algorithm. This algorithm enables the solution of models containing component instance-level dependencies, variables with multiple descriptions in parallel, and correlations modeled as parametric dependencies. We integrate our work into the Descartes Modeling Language (DML), allowing for accurate and efficient modeling and analysis of parametric dependencies. These performance predictions are valuable for various purposes such as capacity planning, bottleneck analysis, configuration optimization and proactive auto-scaling. Our evaluation analyzes a video store application. The prediction for varying language mixes and video sizes shows a mean error below 5% for utilization and below 10% for response time.}, author = {Eismann, Simon and Walter, J{\"u}rgen and von Kistowski, J{\'o}akim and Kounev, Samuel}, booktitle = {2018 IEEE International Conference on Software Architecture (ICSA)}, keywords = {Meta-models}, month = {04}, note = {Acceptance Rate: 25,6\% (22/86)}, pages = {135-144}, title = {{Modeling of Parametric Dependencies for Performance Prediction of Component-based Software Systems at Run-time}}, year = 2018 }

%0 Conference Paper %1 EiWaKiKo2018-ParametricDependencies %A Eismann, Simon %A Walter, Jürgen %A von Kistowski, Jóakim %A Kounev, Samuel %B 2018 IEEE International Conference on Software Architecture (ICSA) %D 2018 %P 135-144 %T Modeling of Parametric Dependencies for Performance Prediction of Component-based Software Systems at Run-time %U https://ieeexplore.ieee.org/document/8417146 %X Model-based performance analysis can be leveraged to explore performance properties of software systems. To capture the behavior of varying workload mixes, configurations, and deployments of a software system requires formal modeling of the impact of configuration parameters and user input on the system behavior. Such influences are represented as parametric dependencies in software performance models. Existing modeling approaches focus on modeling parametric dependencies at design-time. This paper identifies runtime specific parametric dependency features, which are not supported by existing work. Therefore, this paper proposes a novel modeling methodology for parametric dependencies and a corresponding graph-based resolution algorithm. This algorithm enables the solution of models containing component instance-level dependencies, variables with multiple descriptions in parallel, and correlations modeled as parametric dependencies. We integrate our work into the Descartes Modeling Language (DML), allowing for accurate and efficient modeling and analysis of parametric dependencies. These performance predictions are valuable for various purposes such as capacity planning, bottleneck analysis, configuration optimization and proactive auto-scaling. Our evaluation analyzes a video store application. The prediction for varying language mixes and video sizes shows a mean error below 5% for utilization and below 10% for response time.
The Vision of Self-Aware Performance Models. J. Grohmann; S. Eismann; S. Kounev; in 2018 IEEE International Conference on Software Architecture Companion (ICSA-C) (2018). 60–63.
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Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.

@inproceedings{GrEiKo-ICSA18-Vision, abstract = {Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.}, author = {Grohmann, Johannes and Eismann, Simon and Kounev, Samuel}, booktitle = {2018 IEEE International Conference on Software Architecture Companion (ICSA-C)}, keywords = {Self-adaptive-systems}, month = {04}, pages = {60–63}, title = {{The Vision of Self-Aware Performance Models}}, year = 2018 }

%0 Conference Paper %1 GrEiKo-ICSA18-Vision %A Grohmann, Johannes %A Eismann, Simon %A Kounev, Samuel %B 2018 IEEE International Conference on Software Architecture Companion (ICSA-C) %D 2018 %P 60--63 %R 10.1109/ICSA-C.2018.00024 %T The Vision of Self-Aware Performance Models %U https://doi.org/10.1109/ICSA-C.2018.00024 %X Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.
TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research. J. von Kistowski; S. Eismann; N. Schmitt; A. Bauer; J. Grohmann; S. Kounev; in Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (2018). 223–236.
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Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.

@inproceedings{KiEiScBaGrKo2018-MASCOTS-TeaStore, abstract = {Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.}, author = {von Kistowski, J{\'o}akim and Eismann, Simon and Schmitt, Norbert and Bauer, Andr{\'e} and Grohmann, Johannes and Kounev, Samuel}, booktitle = {Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems}, keywords = {Power}, month = {09}, note = {{Acceptance Rate: 29.5\% (23/78)}}, pages = {223–236}, publisher = {IEEE Computer Society}, series = {MASCOTS '18}, title = {{TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research}}, year = 2018 }

%0 Conference Paper %1 KiEiScBaGrKo2018-MASCOTS-TeaStore %A von Kistowski, Jóakim %A Eismann, Simon %A Schmitt, Norbert %A Bauer, André %A Grohmann, Johannes %A Kounev, Samuel %B Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems %D 2018 %I IEEE Computer Society %P 223--236 %R 10.1109/MASCOTS.2018.00030 %T TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research %U https://doi.org/10.1109/MASCOTS.2018.00030 %X Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.
Black-box Learning of Parametric Dependencies for Performance Models. V. Ackermann; J. Grohmann; S. Eismann; S. Kounev; in Proceedings of 13th International Workshop on Models@run.time (MRT), co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018) (2018).
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Modeling parametric dependencies in architectural performance models increases performance prediction accuracy. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which is often infeasible. In this paper, we propose to characterize parametric dependencies based on monitoring data. We create a representative dataset and show that different machine learning approaches perform best, depending on the characteristics of the dependency. Based on these results, we introduce a meta-selector that chooses the most suitable machine learning approach based on the dependency characteristics. In our evaluation, the meta-selector reduces the prediction error compared to the best individual machine learning approach, SVR, by 30%. As a proof of concept, we show that our approach is capable of automatically characterizing a manually modeled dependency from a previous case-study, resulting in a response time prediction accuracy of 92.8%.

@inproceedings{AcGrEiKo2018-MRT-DependencyModeling, abstract = {Modeling parametric dependencies in architectural performance models increases performance prediction accuracy. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which is often infeasible. In this paper, we propose to characterize parametric dependencies based on monitoring data. We create a representative dataset and show that different machine learning approaches perform best, depending on the characteristics of the dependency. Based on these results, we introduce a meta-selector that chooses the most suitable machine learning approach based on the dependency characteristics. In our evaluation, the meta-selector reduces the prediction error compared to the best individual machine learning approach, SVR, by 30%. As a proof of concept, we show that our approach is capable of automatically characterizing a manually modeled dependency from a previous case-study, resulting in a response time prediction accuracy of 92.8%.}, author = {Ackermann, Vanessa and Grohmann, Johannes and Eismann, Simon and Kounev, Samuel}, booktitle = {Proceedings of 13th International Workshop on Models@run.time (MRT), co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018)}, keywords = {Optimization}, month = 10, series = {CEUR Workshop Proceedings}, title = {Black-box Learning of Parametric Dependencies for Performance Models}, year = 2018 }

%0 Conference Paper %1 AcGrEiKo2018-MRT-DependencyModeling %A Ackermann, Vanessa %A Grohmann, Johannes %A Eismann, Simon %A Kounev, Samuel %B Proceedings of 13th International Workshop on Models@run.time (MRT), co-located with ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018) %D 2018 %T Black-box Learning of Parametric Dependencies for Performance Models %U http://ceur-ws.org/Vol-2245/mrt_paper_5.pdf %X Modeling parametric dependencies in architectural performance models increases performance prediction accuracy. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which is often infeasible. In this paper, we propose to characterize parametric dependencies based on monitoring data. We create a representative dataset and show that different machine learning approaches perform best, depending on the characteristics of the dependency. Based on these results, we introduce a meta-selector that chooses the most suitable machine learning approach based on the dependency characteristics. In our evaluation, the meta-selector reduces the prediction error compared to the best individual machine learning approach, SVR, by 30%. As a proof of concept, we show that our approach is capable of automatically characterizing a manually modeled dependency from a previous case-study, resulting in a response time prediction accuracy of 92.8%.

2017[ to top ]

Automated and Adaptable Decision Support for Software Performance Engineering. J. Walter; A. van Hoorn; S. Kounev; in Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools (2017).
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Software performance engineering (SPE) provides a plethora of methods and tooling for measuring, modeling, and evaluating performance properties of software systems. The solution approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. While approaches allow for interchangeability, the choice of an appropriate approach and tooling to solve a given performance concern still relies on expert knowledge. Currently, there is no automated and extensible approach for decision support. In this paper, we present a methodology for the automated selection of performance engineering approaches tailored to user concerns. We decouple the complexity of selecting an SPE approach for a given scenario providing a decision engine and solution approach capability models. This separation allows to easily append additional solution approaches and rating criteria. We demonstrate the applicability by presenting decision engines that compare measurement- and model-based analysis approaches.

@inproceedings{WalterHoKo2017-DecisionSupport, abstract = {Software performance engineering (SPE) provides a plethora of methods and tooling for measuring, modeling, and evaluating performance properties of software systems. The solution approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. While approaches allow for interchangeability, the choice of an appropriate approach and tooling to solve a given performance concern still relies on expert knowledge. Currently, there is no automated and extensible approach for decision support. In this paper, we present a methodology for the automated selection of performance engineering approaches tailored to user concerns. We decouple the complexity of selecting an SPE approach for a given scenario providing a decision engine and solution approach capability models. This separation allows to easily append additional solution approaches and rating criteria. We demonstrate the applicability by presenting decision engines that compare measurement- and model-based analysis approaches.}, author = {Walter, J{\"u}rgen and van Hoorn, Andre and Kounev, Samuel}, booktitle = {Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools}, keywords = {DQL}, month = 12, title = {{Automated and Adaptable Decision Support for Software Performance Engineering}}, year = 2017 }

%0 Conference Paper %1 WalterHoKo2017-DecisionSupport %A Walter, Jürgen %A van Hoorn, Andre %A Kounev, Samuel %B Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools %D 2017 %T Automated and Adaptable Decision Support for Software Performance Engineering %X Software performance engineering (SPE) provides a plethora of methods and tooling for measuring, modeling, and evaluating performance properties of software systems. The solution approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. While approaches allow for interchangeability, the choice of an appropriate approach and tooling to solve a given performance concern still relies on expert knowledge. Currently, there is no automated and extensible approach for decision support. In this paper, we present a methodology for the automated selection of performance engineering approaches tailored to user concerns. We decouple the complexity of selecting an SPE approach for a given scenario providing a decision engine and solution approach capability models. This separation allows to easily append additional solution approaches and rating criteria. We demonstrate the applicability by presenting decision engines that compare measurement- and model-based analysis approaches.
Providing Model-Extraction-as-a-Service for Architectural Performance Models. J. Walter; S. Eismann; N. Reed; S. Kounev; in Proceedings of the 2017 Symposium on Software Performance (SSP) (2017).
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Architectural performance models can be leveraged to explore performance properties of software systems during design-time and run-time. We see a reluctance from industry to adopt model-based analysis approaches due to the required expertise and modeling effort. Building models from scratch in an editor does not scale for medium and large-scale systems in an industrial context. Existing open-source performance model extraction approaches imply signi cant initial efforts which might be challenging for layman users. To simplify usage, we provide the extraction of architectural performance models based on application monitoring traces as a web service. Model-Extraction-as-a-Service (MEaaS) solves the usability problem and lowers the initial effort of applying model-based analysis approaches.

@inproceedings{WaEiReKo2017-SPP-MEaaS, abstract = {Architectural performance models can be leveraged to explore performance properties of software systems during design-time and run-time. We see a reluctance from industry to adopt model-based analysis approaches due to the required expertise and modeling effort. Building models from scratch in an editor does not scale for medium and large-scale systems in an industrial context. Existing open-source performance model extraction approaches imply signi cant initial efforts which might be challenging for layman users. To simplify usage, we provide the extraction of architectural performance models based on application monitoring traces as a web service. Model-Extraction-as-a-Service (MEaaS) solves the usability problem and lowers the initial effort of applying model-based analysis approaches.}, author = {Walter, J{\"u}rgen and Eismann, Simon and Reed, Nikolai and Kounev, Samuel}, booktitle = {Proceedings of the 2017 Symposium on Software Performance (SSP)}, keywords = {t_workshop}, month = 11, title = {{Providing Model-Extraction-as-a-Service for Architectural Performance Models}}, year = 2017 }

%0 Conference Paper %1 WaEiReKo2017-SPP-MEaaS %A Walter, Jürgen %A Eismann, Simon %A Reed, Nikolai %A Kounev, Samuel %B Proceedings of the 2017 Symposium on Software Performance (SSP) %D 2017 %T Providing Model-Extraction-as-a-Service for Architectural Performance Models %X Architectural performance models can be leveraged to explore performance properties of software systems during design-time and run-time. We see a reluctance from industry to adopt model-based analysis approaches due to the required expertise and modeling effort. Building models from scratch in an editor does not scale for medium and large-scale systems in an industrial context. Existing open-source performance model extraction approaches imply signi cant initial efforts which might be challenging for layman users. To simplify usage, we provide the extraction of architectural performance models based on application monitoring traces as a web service. Model-Extraction-as-a-Service (MEaaS) solves the usability problem and lowers the initial effort of applying model-based analysis approaches.
An Expandable Extraction Framework for Architectural Performance Models. J. Walter; C. Stier; H. Koziolek; S. Kounev; in Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS’17) (2017).
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Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model (PCM). For the extracted models we compare simulation results with measurements receiving accurate results.

@inproceedings{WaStKoKo2017-QUDOS-PMXBuilder, abstract = {Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model (PCM). For the extracted models we compare simulation results with measurements receiving accurate results.}, author = {Walter, J{\"u}rgen and Stier, Christian and Koziolek, Heiko and Kounev, Samuel}, booktitle = {{Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17)}}, keywords = {t_workshop}, month = {04}, publisher = {ACM}, title = {{An Expandable Extraction Framework for Architectural Performance Models}}, year = 2017 }

%0 Conference Paper %1 WaStKoKo2017-QUDOS-PMXBuilder %A Walter, Jürgen %A Stier, Christian %A Koziolek, Heiko %A Kounev, Samuel %B Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17) %D 2017 %I ACM %T An Expandable Extraction Framework for Architectural Performance Models %X Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model (PCM). For the extracted models we compare simulation results with measurements receiving accurate results.
Mapping of Service Level Objectives to Performance Queries. J. Walter; D. Okanovic; S. Kounev; in Proceedings of the 2017 Workshop on Challenges in Performance Methods for Software Development (WOSP-C’17) co-located with 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
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The concept of service level agreements (SLAs) defines the idea of a reliable contract between service providers and their users. SLAs provide information on the scope, the quality and the responsibilities of a service and its provider. Service level objectives (SLOs) define the detailed, measurable conditions of the SLAs. After service deployment, SLAs are monitored, to assess potentially dangerous situations, that further lead to violation of the SLAs. However, the SLA monitoring infrastructure is usually specific to the underlying system infrastructure, lacks generalization, and is often limited to measurement-based approaches. This makes it hard to apply the results from SLA monitoring in other stages of the software life-cycle. In this paper we propose the mapping of concerns defined in SLAs to the performance metrics queries using the Descartes Query Language (DQL). The benefit of our approach is that the same performance query can then be reused for evaluation of performance concerns throughout the entire life-cycle, and regardless of which approach is used for evaluation.

@inproceedings{WaOkKo2017-WOSPC-SLO2Queries, abstract = {The concept of service level agreements (SLAs) defines the idea of a reliable contract between service providers and their users. SLAs provide information on the scope, the quality and the responsibilities of a service and its provider. Service level objectives (SLOs) define the detailed, measurable conditions of the SLAs. After service deployment, SLAs are monitored, to assess potentially dangerous situations, that further lead to violation of the SLAs. However, the SLA monitoring infrastructure is usually specific to the underlying system infrastructure, lacks generalization, and is often limited to measurement-based approaches. This makes it hard to apply the results from SLA monitoring in other stages of the software life-cycle. In this paper we propose the mapping of concerns defined in SLAs to the performance metrics queries using the Descartes Query Language (DQL). The benefit of our approach is that the same performance query can then be reused for evaluation of performance concerns throughout the entire life-cycle, and regardless of which approach is used for evaluation.}, author = {Walter, J{\"u}rgen and Okanovic, Dusan and Kounev, Samuel}, booktitle = {{Proceedings of the 2017 Workshop on Challenges in Performance Methods for Software Development (WOSP-C'17) co-located with 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017)}}, keywords = {DQL}, month = {04}, publisher = {ACM}, title = {{Mapping of Service Level Objectives to Performance Queries}}, year = 2017 }

%0 Conference Paper %1 WaOkKo2017-WOSPC-SLO2Queries %A Walter, Jürgen %A Okanovic, Dusan %A Kounev, Samuel %B Proceedings of the 2017 Workshop on Challenges in Performance Methods for Software Development (WOSP-C'17) co-located with 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) %D 2017 %I ACM %T Mapping of Service Level Objectives to Performance Queries %X The concept of service level agreements (SLAs) defines the idea of a reliable contract between service providers and their users. SLAs provide information on the scope, the quality and the responsibilities of a service and its provider. Service level objectives (SLOs) define the detailed, measurable conditions of the SLAs. After service deployment, SLAs are monitored, to assess potentially dangerous situations, that further lead to violation of the SLAs. However, the SLA monitoring infrastructure is usually specific to the underlying system infrastructure, lacks generalization, and is often limited to measurement-based approaches. This makes it hard to apply the results from SLA monitoring in other stages of the software life-cycle. In this paper we propose the mapping of concerns defined in SLAs to the performance metrics queries using the Descartes Query Language (DQL). The benefit of our approach is that the same performance query can then be reused for evaluation of performance concerns throughout the entire life-cycle, and regardless of which approach is used for evaluation.
Design and Evaluation of a Proactive, Application-Aware Auto-Scaler. A. Bauer; N. Herbst; S. Kounev; in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
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@inproceedings{BaHeKo2017-ICPE-Auto-Scaler-Tutorial, author = {Bauer, Andr{\'e} and Herbst, Nikolas and Kounev, Samuel}, booktitle = {{Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017)}}, keywords = {LIMBO}, month = {04}, title = {{Design and Evaluation of a Proactive, Application-Aware Auto-Scaler}}, year = 2017 }

%0 Conference Paper %1 BaHeKo2017-ICPE-Auto-Scaler-Tutorial %A Bauer, André %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) %D 2017 %T Design and Evaluation of a Proactive, Application-Aware Auto-Scaler
Solving Explicit Dependencies for Architectural Performance Models. S. Eismann; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, März).
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@mastersthesis{Eismann2017Solving, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Eismann, Simon}, keywords = {Thesis_supervised_by_Juergen_Walter}, month = {03}, note = {<b>ASQF Award</b>}, school = {University of W{\"u}rzburg}, title = {{Solving Explicit Dependencies for Architectural Performance Models}}, type = {{Master Thesis}}, year = 2017 }

%0 Thesis %1 Eismann2017Solving %A Eismann, Simon %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2017 %T Solving Explicit Dependencies for Architectural Performance Models
Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language. S. Kounev; N. Huber; F. Brosig; S. Spinner; M. Baehr; in Jan Jürjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany (2017).
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- [ EndNote ]
@inproceedings{KoHuBrSpBa2017-SE-DML, address = {Hannover, Germany}, author = {Kounev, Samuel and Huber, Nikolaus and Brosig, Fabian and Spinner, Simon and Baehr, Manuel}, booktitle = {Jan J{\"u}rjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany}, keywords = {descartes}, month = {02}, publisher = {Gesellschaft f{\"u}r Informatik (GI)}, series = {Lecture Notes in Informatics (LNI)}, title = {{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}}, year = 2017 }

%0 Conference Paper %1 KoHuBrSpBa2017-SE-DML %A Kounev, Samuel %A Huber, Nikolaus %A Brosig, Fabian %A Spinner, Simon %A Baehr, Manuel %B Jan Jürjens, Kurt Schneider (Hrsg.): Software Engineering 2017 (SE 2017), Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Germany %C Hannover, Germany %D 2017 %I Gesellschaft für Informatik (GI) %T Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language
Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language. N. Huber; F. Brosig; S. Spinner; S. Kounev; M. Bähr; in IEEE Transactions on Software Engineering (TSE) (2017). 43(5) 432–452.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.

@article{HuBrSpKoBa2017-TSE-DML, abstract = {Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.}, author = {Huber, Nikolaus and Brosig, Fabian and Spinner, Simon and Kounev, Samuel and B{\"a}hr, Manuel}, journal = {IEEE Transactions on Software Engineering (TSE)}, keywords = {DQL}, number = 5, pages = {432–452}, publisher = {IEEE Computer Society}, title = {{Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language}}, volume = 43, year = 2017 }

%0 Journal Article %1 HuBrSpKoBa2017-TSE-DML %A Huber, Nikolaus %A Brosig, Fabian %A Spinner, Simon %A Kounev, Samuel %A Bähr, Manuel %D 2017 %I IEEE Computer Society %J IEEE Transactions on Software Engineering (TSE) %N 5 %P 432--452 %T Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language %U http://dx.doi.org/10.1109/TSE.2016.2613863 %V 43 %X Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.

2016[ to top ]

The Descartes Modeling Language for Self-Aware Performance and Resource Management. S. Kounev; (Z. M. (Jack) Jiang, Hrsg.) (2016, April).
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@misc{DML_IEEESoftwareblog2016, author = {Kounev, Samuel}, editor = {Jiang, Zhen Ming (Jack)}, keywords = {Self-adaptive-systems}, month = {04}, note = {IEEE Software Blog}, title = {{The Descartes Modeling Language for Self-Aware Performance and Resource Management}}, year = 2016 }

%0 Generic %1 DML_IEEESoftwareblog2016 %A Kounev, Samuel %D 2016 %E Jiang, Zhen Ming (Jack) %T The Descartes Modeling Language for Self-Aware Performance and Resource Management %U http://blog.ieeesoftware.org/2016/04/the-descartes-modeling-language-for.html
A Reference Architecture for Online Performance Model Extraction in Virtualized Environments. S. Spinner; J. Walter; S. Kounev; in Proceedings of the 2016 Workshop on Challenges in Performance Methods for Software Development (WOSP-C’16) co-located with 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) (2016).
- [ Abstract ]
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Performance models can support decisions throughout the life-cycle of a software system. However, the manual construction of such performance models is a complex and time-consuming task requiring deep system knowledge. Therefore, automatic approaches for creating and updating performance models of a running system are necessary. Existing work focuses on single aspects of model extraction or proposes approaches specifically designed for a certain technology stack. In virtualized environments, we often see different applications based on diverse technology stacks sharing the same infrastructure. In order to enable online performance model extraction in such environments, we describe a new reference architecture for integrating different specialized model extraction solutions.

@inproceedings{SpWaKo2015-WOSPC-ReferenceArchitecture, abstract = {Performance models can support decisions throughout the life-cycle of a software system. However, the manual construction of such performance models is a complex and time-consuming task requiring deep system knowledge. Therefore, automatic approaches for creating and updating performance models of a running system are necessary. Existing work focuses on single aspects of model extraction or proposes approaches specifically designed for a certain technology stack. In virtualized environments, we often see different applications based on diverse technology stacks sharing the same infrastructure. In order to enable online performance model extraction in such environments, we describe a new reference architecture for integrating different specialized model extraction solutions.}, author = {Spinner, Simon and Walter, J{\"u}rgen and Kounev, Samuel}, booktitle = {{Proceedings of the 2016 Workshop on Challenges in Performance Methods for Software Development (WOSP-C'16) co-located with 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016)}}, keywords = {t_workshop}, month = {03}, title = {{A Reference Architecture for Online Performance Model Extraction in Virtualized Environments}}, year = 2016 }

%0 Conference Paper %1 SpWaKo2015-WOSPC-ReferenceArchitecture %A Spinner, Simon %A Walter, Jürgen %A Kounev, Samuel %B Proceedings of the 2016 Workshop on Challenges in Performance Methods for Software Development (WOSP-C'16) co-located with 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) %D 2016 %T A Reference Architecture for Online Performance Model Extraction in Virtualized Environments %X Performance models can support decisions throughout the life-cycle of a software system. However, the manual construction of such performance models is a complex and time-consuming task requiring deep system knowledge. Therefore, automatic approaches for creating and updating performance models of a running system are necessary. Existing work focuses on single aspects of model extraction or proposes approaches specifically designed for a certain technology stack. In virtualized environments, we often see different applications based on diverse technology stacks sharing the same infrastructure. In order to enable online performance model extraction in such environments, we describe a new reference architecture for integrating different specialized model extraction solutions.
Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. A. Bauer; (2016, Oktober).
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@misc{talk-Bauer-InvSpec16, author = {Bauer, Andr{\'e}}, howpublished = {Invited Talk, Spec Research - Cloud Working Group}, keywords = {Application-aware}, month = 10, title = {{Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism}}, year = 2016 }

%0 Generic %1 talk-Bauer-InvSpec16 %A Bauer, André %D 2016 %T Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism
A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures. S. Kounev; N. Huber; F. Brosig; X. Zhu; in IEEE Computer (2016). 49(7) 53–61.
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@article{KoHuBrZh2016-IEEEComputer-Descartes, author = {Kounev, Samuel and Huber, Nikolaus and Brosig, Fabian and Zhu, Xiaoyun}, journal = {IEEE Computer}, keywords = {Self-adaptive-systems}, month = {07}, number = 7, pages = {53-61}, publisher = {IEEE}, title = {{A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures}}, volume = 49, year = 2016 }

%0 Journal Article %1 KoHuBrZh2016-IEEEComputer-Descartes %A Kounev, Samuel %A Huber, Nikolaus %A Brosig, Fabian %A Zhu, Xiaoyun %D 2016 %I IEEE %J IEEE Computer %N 7 %P 53-61 %T A Model-Based Approach to Designing Self-Aware IT Systems and Infrastructures %U http://dx.doi.org/10.1109/MC.2016.198 %V 49
Transformation of Descartes Modeling Language to Queueing Networks. K. Dietz; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, September).
- [ Abstract ]
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Preventing performance problems is a challenging task for todays IT systems. Model-based performance prediction can be used to ensure quality of service. The Descartes Modeling Language (DML) is an architectural performance modeling language which is solved through transformations to predictive models. In this thesis we propose a transformation from DML to Queueing Networks (QNs), a predictive modeling formalism. This enables to solve DML models based on QN analysis approaches. QNs are highly researched and offer various analysis tools and apporaches.

@mastersthesis{Dietz2016, abstract = {Preventing performance problems is a challenging task for todays IT systems. Model-based performance prediction can be used to ensure quality of service. The Descartes Modeling Language (DML) is an architectural performance modeling language which is solved through transformations to predictive models. In this thesis we propose a transformation from DML to Queueing Networks (QNs), a predictive modeling formalism. This enables to solve DML models based on QN analysis approaches. QNs are highly researched and offer various analysis tools and apporaches.}, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Dietz, Katharina}, keywords = {Thesis_supervised_by_Juergen_Walter}, month = {09}, school = {University of W{\"u}rzburg}, title = {{Transformation of Descartes Modeling Language to Queueing Networks}}, type = {{Bachelor Thesis}}, year = 2016 }

%0 Thesis %1 Dietz2016 %A Dietz, Katharina %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2016 %T Transformation of Descartes Modeling Language to Queueing Networks %X Preventing performance problems is a challenging task for todays IT systems. Model-based performance prediction can be used to ensure quality of service. The Descartes Modeling Language (DML) is an architectural performance modeling language which is solved through transformations to predictive models. In this thesis we propose a transformation from DML to Queueing Networks (QNs), a predictive modeling formalism. This enables to solve DML models based on QN analysis approaches. QNs are highly researched and offer various analysis tools and apporaches.

2015[ to top ]

Automated Transformation of Descartes Modeling Language to Palladio Component Model. J. Walter; S. Eismann; A. Hildebrandt; in Proceedings of the 2015 Symposium on Software Performance (SSP) (2015).
- [ Abstract ]
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Model-based performance predictions and reconfigurations enable optimizing resource efficiency while ensuring that Quality-of-Service demands are met in today's complex ITsystems. The Descartes Modeling Language (DML) and the Palladio Component Model (PCM) are two architectural performance modeling formalisms applied in this context. This paper compares DML to PCM concerning similarities, differences and semantic gaps. Based on this, we propose a mapping from DML to PCM for which we implemented a tool realizing an automated transformation.

@inproceedings{WaEiHi2015-SPP-DML2PCM, abstract = {Model-based performance predictions and reconfigurations enable optimizing resource efficiency while ensuring that Quality-of-Service demands are met in today's complex ITsystems. The Descartes Modeling Language (DML) and the Palladio Component Model (PCM) are two architectural performance modeling formalisms applied in this context. This paper compares DML to PCM concerning similarities, differences and semantic gaps. Based on this, we propose a mapping from DML to PCM for which we implemented a tool realizing an automated transformation.}, author = {Walter, J{\"u}rgen and Eismann, Simon and Hildebrandt, Adrian}, booktitle = {Proceedings of the 2015 Symposium on Software Performance (SSP)}, keywords = {t_workshop}, month = 11, title = {{Automated Transformation of Descartes Modeling Language to Palladio Component Model}}, year = 2015 }

%0 Conference Paper %1 WaEiHi2015-SPP-DML2PCM %A Walter, Jürgen %A Eismann, Simon %A Hildebrandt, Adrian %B Proceedings of the 2015 Symposium on Software Performance (SSP) %D 2015 %T Automated Transformation of Descartes Modeling Language to Palladio Component Model %X Model-based performance predictions and reconfigurations enable optimizing resource efficiency while ensuring that Quality-of-Service demands are met in today's complex ITsystems. The Descartes Modeling Language (DML) and the Palladio Component Model (PCM) are two architectural performance modeling formalisms applied in this context. This paper compares DML to PCM concerning similarities, differences and semantic gaps. Based on this, we propose a mapping from DML to PCM for which we implemented a tool realizing an automated transformation.
Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study. N. Huber; J. Walter; M. Bähr; S. Kounev; in Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC) (2015).
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Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. Blue Yonder is such a service provider in the field of predictive analytics, looking for novel approaches to automatically adapt their service infrastructure to changing customer workloads and service-level agreements. In this paper, we present a realistic case study in which we apply a holistic modelbased approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. As modeling formalism, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. This case study is an exemplar for applying a holistic model-based approach to engineering performance-aware system adaptation. More specifically, the results evaluate the applicability and effectiveness of our approach and demonstrate that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management.

@inproceedings{HuWaBaKo2015-ICCAC-BlueYonder, abstract = {Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. Blue Yonder is such a service provider in the field of predictive analytics, looking for novel approaches to automatically adapt their service infrastructure to changing customer workloads and service-level agreements. In this paper, we present a realistic case study in which we apply a holistic modelbased approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. As modeling formalism, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. This case study is an exemplar for applying a holistic model-based approach to engineering performance-aware system adaptation. More specifically, the results evaluate the applicability and effectiveness of our approach and demonstrate that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management.}, author = {Huber, Nikolaus and Walter, J{\"u}rgen and B{\"a}hr, Manuel and Kounev, Samuel}, booktitle = {Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC)}, keywords = {Self-adaptive-systems}, month = {09}, publisher = {IEEE}, title = {{Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study}}, year = 2015 }

%0 Conference Paper %1 HuWaBaKo2015-ICCAC-BlueYonder %A Huber, Nikolaus %A Walter, Jürgen %A Bähr, Manuel %A Kounev, Samuel %B Proceedings of the 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC) %D 2015 %I IEEE %T Model-based Autonomic and Performance-aware System Adaptation in Heterogeneous Resource Environments: A Case Study %X Recent trends like cloud computing show that service providers increasingly adopt to modern self-adaptive system architectures promising higher resource efficiency and lower operating costs. Blue Yonder is such a service provider in the field of predictive analytics, looking for novel approaches to automatically adapt their service infrastructure to changing customer workloads and service-level agreements. In this paper, we present a realistic case study in which we apply a holistic modelbased approach to build a self-adaptive system that automatically maintains performance requirements and resource efficiency in the heterogeneous resource environment of Blue Yonder. As modeling formalism, we employ the Descartes Modeling Language (DML), a domain-specific language for modeling the performance behavior and run-time adaptation processes of modern dynamic IT systems. This case study is an exemplar for applying a holistic model-based approach to engineering performance-aware system adaptation. More specifically, the results evaluate the applicability and effectiveness of our approach and demonstrate that DML provides suitable modeling abstractions that can be used as a basis for self-adaptive performance and resource management.
The Descartes Modeling Language for Self-Aware Performance and Resource Management. S. Kounev; F. Brosig; N. Huber; in Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. März 2015, Dresden, Deutschland (2015).
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@inproceedings{KoBrHu2015-SE-SelfAwareDescartes, address = {Bonn, Germany}, author = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus}, booktitle = {Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. M{\"a}rz 2015, Dresden, Deutschland}, keywords = {Self-aware-computing}, month = {03}, publisher = {GI}, series = {Lecture Notes in Informatics (LNI)}, title = {{The Descartes Modeling Language for Self-Aware Performance and Resource Management}}, year = 2015 }

%0 Conference Paper %1 KoBrHu2015-SE-SelfAwareDescartes %A Kounev, Samuel %A Brosig, Fabian %A Huber, Nikolaus %B Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. März 2015, Dresden, Deutschland %C Bonn, Germany %D 2015 %I GI %T The Descartes Modeling Language for Self-Aware Performance and Resource Management
The Descartes Modeling Language for Self-Aware Performance and Resource Management. S. Kounev; (2015, März).
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@misc{Talk-Ko2015-SE-SelfAwareDescartes, author = {Kounev, Samuel}, howpublished = {Software Engineering 2015, Fachtagung des GI-Fachbereichs Softwaretechnik, 17. - 20. M{\"a}rz 2015, Dresden, Germany}, keywords = {Talks}, month = {03}, title = {{The Descartes Modeling Language for Self-Aware Performance and Resource Management}}, year = 2015 }

%0 Generic %1 Talk-Ko2015-SE-SelfAwareDescartes %A Kounev, Samuel %D 2015 %T The Descartes Modeling Language for Self-Aware Performance and Resource Management
Automated Transformation of Descartes Modeling Language to Palladio Component Models. A. Hildebrandt; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, Januar).
- [ BibTeX ]
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@mastersthesis{Hildebrandt2014, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hildebrandt, Adrian}, keywords = {Thesis_supervised_by_Juergen_Walter}, month = {01}, school = {University of W{\"u}rzburg}, title = {{Automated Transformation of Descartes Modeling Language to Palladio Component Models}}, type = {{Bachelor Thesis}}, year = 2015 }

%0 Thesis %1 Hildebrandt2014 %A Hildebrandt, Adrian %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2015 %T Automated Transformation of Descartes Modeling Language to Palladio Component Models

2014[ to top ]

Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments. N. Huber; A. van Hoorn; A. Koziolek; F. Brosig; S. Kounev; in Service Oriented Computing and Applications Journal (SOCA) (2014). 8(1) 73–89.
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@article{HuHoKoBrKo2014-SOCA-ModelingRuntimeAdaptation, author = {Huber, Nikolaus and van Hoorn, Andr{\'e} and Koziolek, Anne and Brosig, Fabian and Kounev, Samuel}, journal = {Service Oriented Computing and Applications Journal (SOCA)}, keywords = {Self-adaptive-systems}, month = {{March}}, number = 1, pages = {73–89}, publisher = {Springer-Verlag}, title = {{Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments}}, volume = 8, year = 2014 }

%0 Journal Article %1 HuHoKoBrKo2014-SOCA-ModelingRuntimeAdaptation %A Huber, Nikolaus %A van Hoorn, André %A Koziolek, Anne %A Brosig, Fabian %A Kounev, Samuel %D 2014 %I Springer-Verlag %J Service Oriented Computing and Applications Journal (SOCA) %N 1 %P 73--89 %T Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments %V 8
The Descartes Modeling Language S. Kounev; F. Brosig; N. Huber; (2014).
- [ Abstract ]
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This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.

@techreport{KoBrHu2014-TechReport-DML, abstract = {{This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.}}, author = {Kounev, Samuel and Brosig, Fabian and Huber, Nikolaus}, institution = {{Department of Computer Science, University of Wuerzburg}}, keywords = {Self-adaptive-systems}, month = 10, title = {{The Descartes Modeling Language}}, year = 2014 }

%0 Report %1 KoBrHu2014-TechReport-DML %A Kounev, Samuel %A Brosig, Fabian %A Huber, Nikolaus %D 2014 %T The Descartes Modeling Language %U http://www.descartes-research.net/dml/ %X This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.
Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples P. Rygielski; S. Kounev; (2014).
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The goal of this document is to present the DNI Meta-Model, its instances, sub-meta-models, and model transformations in more details than in an usual conference or journal paper. We describe the meta-models and the transformations in details, give example of systems modeled using the meta-models, and present the transformations in a step-by-step manner.

@techreport{RyKo-DNI-Manual-TR, abstract = {The goal of this document is to present the DNI Meta-Model, its instances, sub-meta-models, and model transformations in more details than in an usual conference or journal paper. We describe the meta-models and the transformations in details, give example of systems modeled using the meta-models, and present the transformations in a step-by-step manner.}, address = {Am Hubland, 97074 W{\"u}rzburg}, author = {Rygielski, Piotr and Kounev, Samuel}, institution = {Chair of Software Engineering, University of W{\"u}rzburg}, keywords = {DNI}, month = {09}, title = {{Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples}}, type = {{Technical Report v.0.3}}, year = 2014 }

%0 Report %1 RyKo-DNI-Manual-TR %A Rygielski, Piotr %A Kounev, Samuel %C Am Hubland, 97074 Würzburg %D 2014 %T Descartes Network Infrastructures (DNI) Manual: Meta-models, Transformations, Examples %U http://se.informatik.uni-wuerzburg.de/staff/piotr_rygielski/dni_manual/ %X The goal of this document is to present the DNI Meta-Model, its instances, sub-meta-models, and model transformations in more details than in an usual conference or journal paper. We describe the meta-models and the transformations in details, give example of systems modeled using the meta-models, and present the transformations in a step-by-step manner.
Architecture-Level Software Performance Abstractions for Online Performance Prediction. F. Brosig; N. Huber; S. Kounev; in Elsevier Science of Computer Programming Journal (SciCo) (2014). Vol. 90, Part B 71–92.
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@article{BrHuKo2013-SciCo-SoftwarePerformanceAbstractions, author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel}, journal = {Elsevier Science of Computer Programming Journal (SciCo)}, keywords = {t_journalmagazine}, month = {09}, pages = {71–92}, publisher = {Elsevier}, title = {{Architecture-Level Software Performance Abstractions for Online Performance Prediction}}, volume = {Vol. 90, Part B}, year = 2014 }

%0 Journal Article %1 BrHuKo2013-SciCo-SoftwarePerformanceAbstractions %A Brosig, Fabian %A Huber, Nikolaus %A Kounev, Samuel %D 2014 %I Elsevier %J Elsevier Science of Computer Programming Journal (SciCo) %P 71--92 %T Architecture-Level Software Performance Abstractions for Online Performance Prediction %U http://authors.elsevier.com/sd/article/S0167642313001421 %V Vol. 90, Part B
Architecture-Level Software Performance Models for Online Performance Prediction. F. Brosig; Thesis; Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. (2014, Juli).
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@phdthesis{Brosig2014-KIT-OnlinePerformancePrediction, author = {Brosig, Fabian}, keywords = {Prediction}, month = {07}, note = {<b>{Distinction}</b>}, school = {Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany}, title = {Architecture-Level Software Performance Models for Online Performance Prediction}, year = 2014 }

%0 Thesis %1 Brosig2014-KIT-OnlinePerformancePrediction %A Brosig, Fabian %D 2014 %T Architecture-Level Software Performance Models for Online Performance Prediction %U http://nbn-resolving.org/urn:nbn:de:swb:90-435372
Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures. N. Huber; Thesis; Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. (2014, Juli).
- [ BibTeX ]
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@phdthesis{Huber2014-KIT-AutonomicPerformanceAwareResourceManagement, author = {Huber, Nikolaus}, keywords = {Self-adaptive-systems}, month = {07}, school = {Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany}, title = {Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures}, year = 2014 }

%0 Thesis %1 Huber2014-KIT-AutonomicPerformanceAwareResourceManagement %A Huber, Nikolaus %D 2014 %T Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures %U http://nbn-resolving.org/urn:nbn:de:swb:90-432462

2012[ to top ]

S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures. N. Huber; A. van Hoorn; A. Koziolek; F. Brosig; S. Kounev; in Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012) (2012). 70–77.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.

@inproceedings{HuHoKoBrKo2012-ICEBE-STA, abstract = {Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.}, address = {Los Alamitos, CA, USA}, author = {Huber, Nikolaus and van Hoorn, Andr{\'e} and Koziolek, Anne and Brosig, Fabian and Kounev, Samuel}, booktitle = {Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012)}, keywords = {Optimization}, month = {09}, note = {Acceptance Rate (Full Paper): 19.7\% (26/132)}, pages = {70–77}, publisher = {IEEE Computer Society}, title = {{S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures}}, year = 2012 }

%0 Conference Paper %1 HuHoKoBrKo2012-ICEBE-STA %A Huber, Nikolaus %A van Hoorn, André %A Koziolek, Anne %A Brosig, Fabian %A Kounev, Samuel %B Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012) %C Los Alamitos, CA, USA %D 2012 %I IEEE Computer Society %P 70--77 %T S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures %U http://conferences.computer.org/icebe/2012/index.htm %X Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.
Modeling Dynamic Virtualized Resource Landscapes. N. Huber; F. Brosig; S. Kounev; in Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012) (2012). 81–90.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
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Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.

@inproceedings{HuBrKo2012-QoSA-ModelingVirtResLandscapes, abstract = {Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.}, address = {New York, NY, USA}, author = {Huber, Nikolaus and Brosig, Fabian and Kounev, Samuel}, booktitle = {Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012)}, keywords = {Self-adaptive-systems}, month = {06}, note = {Acceptance Rate (Full Paper): 25.6\%}, pages = {81–90}, publisher = {ACM}, title = {{Modeling Dynamic Virtualized Resource Landscapes}}, year = 2012 }

%0 Conference Paper %1 HuBrKo2012-QoSA-ModelingVirtResLandscapes %A Huber, Nikolaus %A Brosig, Fabian %A Kounev, Samuel %B Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012) %C New York, NY, USA %D 2012 %I ACM %P 81--90 %T Modeling Dynamic Virtualized Resource Landscapes %U http://qosa.ipd.kit.edu/qosa_2012/ %X Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.
Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models. F. Brosig; N. Huber; S. Kounev; in Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy (2012).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

@inproceedings{BrHuKo2012-CBSE-ParamAndContextDep, abstract = {Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.}, author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel}, booktitle = {Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26–28, 2012, Bertinoro, Italy}, keywords = {Application_quality_of_service_management}, month = {06}, note = {Acceptance Rate (Full Paper): 28.5\%.}, title = {{Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models}}, year = 2012 }

%0 Conference Paper %1 BrHuKo2012-CBSE-ParamAndContextDep %A Brosig, Fabian %A Huber, Nikolaus %A Kounev, Samuel %B Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy %D 2012 %T Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models %U http://cbse-conferences.org/2012/ %X Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

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