Bibliography

• 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.

• 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.

• Enabling Fluid Analysis for Queueing Petri Nets via Model Transformation. C. Müller; P. Rygielski; S. Spinner; S. Kounev; in Electronic Notes in Theoretical Computer Science (2016). 327 71–91.
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  Abstract Due to the growing size of modern IT} systems, their performance analysis becomes an even more challenging task. Existing simulators are unable to analyze the behavior of large systems in a reasonable time, whereas analytical methods suffer from the state space explosion problem. Fluid analysis techniques can be used to approximate the solution of high-order Markov chain models enabling time efficient analysis of large performance models. In this paper, we describe a model-to-model transformation from queueing Petri nets (QPN) into layered queueing networks (LQN). Obtained LQN} models can benefit from three existing solvers: LINE, LQNS, LQSIM. LINE} internally utilize fluid limits approximation to speed up the solving process for large models. We present the incentives for developing the automated model-to-model transformation and present a systematic approach that we followed in its design. We demonstrate the transformations using representative examples. Finally, we evaluate and compare the performance predictions of existing analytical, simulation and fluid analysis solvers. We analyze solvers' limitations, solving time, and memory consumption.

  @article{MuRySpKo2016-PASM-QPN-LQN-Transformation, abstract = {Abstract Due to the growing size of modern \{IT\} systems, their performance analysis becomes an even more challenging task. Existing simulators are unable to analyze the behavior of large systems in a reasonable time, whereas analytical methods suffer from the state space explosion problem. Fluid analysis techniques can be used to approximate the solution of high-order Markov chain models enabling time efficient analysis of large performance models. In this paper, we describe a model-to-model transformation from queueing Petri nets (QPN) into layered queueing networks (LQN). Obtained \{LQN\} models can benefit from three existing solvers: LINE, LQNS, LQSIM. \{LINE\} internally utilize fluid limits approximation to speed up the solving process for large models. We present the incentives for developing the automated model-to-model transformation and present a systematic approach that we followed in its design. We demonstrate the transformations using representative examples. Finally, we evaluate and compare the performance predictions of existing analytical, simulation and fluid analysis solvers. We analyze solvers' limitations, solving time, and memory consumption.}, author = {M{\"u}ller, Christoph and Rygielski, Piotr and Spinner, Simon and Kounev, Samuel}, journal = {Electronic Notes in Theoretical Computer Science}, keywords = {QPN}, note = {The 8th International Workshop on Practical Application of Stochastic Modeling, PASM 2016}, pages = {71–91}, publisher = {Elsevier}, title = {{Enabling Fluid Analysis for Queueing Petri Nets via Model Transformation}}, volume = 327, year = 2016 }

  %0 Journal Article %1 MuRySpKo2016-PASM-QPN-LQN-Transformation %A Müller, Christoph %A Rygielski, Piotr %A Spinner, Simon %A Kounev, Samuel %D 2016 %I Elsevier %J Electronic Notes in Theoretical Computer Science %P 71--91 %T Enabling Fluid Analysis for Queueing Petri Nets via Model Transformation %U http://www.sciencedirect.com/science/article/pii/S157106611630069X %V 327 %X Abstract Due to the growing size of modern IT} systems, their performance analysis becomes an even more challenging task. Existing simulators are unable to analyze the behavior of large systems in a reasonable time, whereas analytical methods suffer from the state space explosion problem. Fluid analysis techniques can be used to approximate the solution of high-order Markov chain models enabling time efficient analysis of large performance models. In this paper, we describe a model-to-model transformation from queueing Petri nets (QPN) into layered queueing networks (LQN). Obtained LQN} models can benefit from three existing solvers: LINE, LQNS, LQSIM. LINE} internally utilize fluid limits approximation to speed up the solving process for large models. We present the incentives for developing the automated model-to-model transformation and present a systematic approach that we followed in its design. We demonstrate the transformations using representative examples. Finally, we evaluate and compare the performance predictions of existing analytical, simulation and fluid analysis solvers. We analyze solvers' limitations, solving time, and memory consumption.

• Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-based Architectures. F. Brosig; P. Meier; S. Becker; A. Koziolek; H. Koziolek; S. Kounev; in IEEE Transactions on Software Engineering (TSE) (2015). 41(2) 157–175.
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  @article{BrMeBeKoKoKo2014-TSE-ModelSolutionTradeOffs, author = {Brosig, Fabian and Meier, Philipp and Becker, Steffen and Koziolek, Anne and Koziolek, Heiko and Kounev, Samuel}, journal = {IEEE Transactions on Software Engineering (TSE)}, keywords = {QPN}, month = {{February}}, number = 2, pages = {157–175}, publisher = {IEEE}, title = {{Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-based Architectures}}, volume = 41, year = 2015 }

  %0 Journal Article %1 BrMeBeKoKoKo2014-TSE-ModelSolutionTradeOffs %A Brosig, Fabian %A Meier, Philipp %A Becker, Steffen %A Koziolek, Anne %A Koziolek, Heiko %A Kounev, Samuel %D 2015 %I IEEE %J IEEE Transactions on Software Engineering (TSE) %N 2 %P 157--175 %T Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-based Architectures %U http://dx.doi.org/10.1109/TSE.2014.2362755 %V 41
• Parallel Simulation of Queueing Petri Nets. J. Walter; S. Spinner; S. Kounev; in Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques (SIMUTools 2015) (2015).
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  Queueing Petri nets show high accurracy to model and analyze cloud scenarios. To apply these models in online scenarios a speedup of simulation is desirable. One way to speedup simulation is parallelization. Decades of intensive research showed no general solution for parallel discrete event simulation, which provides reasonable speedups. We research the application of parallel simulation for cloud scenarios. We develop a parallel simulation engine that shows speedup for certain scenarios common in cloud models. We present a parallel simualtion engine that employs application level and event level parallelism. We parallelize the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.

  @inproceedings{WaSpKo-SIMUTools-QPNParallelSimulation, abstract = {Queueing Petri nets show high accurracy to model and analyze cloud scenarios. To apply these models in online scenarios a speedup of simulation is desirable. One way to speedup simulation is parallelization. Decades of intensive research showed no general solution for parallel discrete event simulation, which provides reasonable speedups. We research the application of parallel simulation for cloud scenarios. We develop a parallel simulation engine that shows speedup for certain scenarios common in cloud models. We present a parallel simualtion engine that employs application level and event level parallelism. We parallelize the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.}, author = {Walter, J{\"u}rgen and Spinner, Simon and Kounev, Samuel}, booktitle = {Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques (SIMUTools 2015)}, keywords = {QPN}, month = {08}, title = {{Parallel Simulation of Queueing Petri Nets}}, year = 2015 }

  %0 Conference Paper %1 WaSpKo-SIMUTools-QPNParallelSimulation %A Walter, Jürgen %A Spinner, Simon %A Kounev, Samuel %B Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques (SIMUTools 2015) %D 2015 %T Parallel Simulation of Queueing Petri Nets %X Queueing Petri nets show high accurracy to model and analyze cloud scenarios. To apply these models in online scenarios a speedup of simulation is desirable. One way to speedup simulation is parallelization. Decades of intensive research showed no general solution for parallel discrete event simulation, which provides reasonable speedups. We research the application of parallel simulation for cloud scenarios. We develop a parallel simulation engine that shows speedup for certain scenarios common in cloud models. We present a parallel simualtion engine that employs application level and event level parallelism. We parallelize the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.
• Declarative Automation Framework for QPME based on DQL. S. Eismann; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, März).
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  @mastersthesis{Eismann2015, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Eismann, Simon}, keywords = {QPN}, month = {03}, school = {University of W{\"u}rzburg}, title = {{Declarative Automation Framework for QPME based on DQL}}, type = {{Bachelor Thesis}}, year = 2015 }

  %0 Thesis %1 Eismann2015 %A Eismann, Simon %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2015 %T Declarative Automation Framework for QPME based on DQL

• Modeling of I/O Performance Interference in Virtualized Environments with Queueing Petri Nets. Q. Noorshams; K. Rostami; S. Kounev; R. Reussner; in Proceedings of the IEEE 22nd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (2014).
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  @inproceedings{NoRoKoRe2014-MASCOTS-IOQPNs, author = {Noorshams, Qais and Rostami, Kiana and Kounev, Samuel and Reussner, Ralf}, booktitle = {Proceedings of the IEEE 22nd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems}, keywords = {Virtualization}, month = {{September}}, series = {MASCOTS 2014}, title = {{Modeling of I/O Performance Interference in Virtualized Environments with Queueing Petri Nets}}, year = 2014 }

  %0 Conference Paper %1 NoRoKoRe2014-MASCOTS-IOQPNs %A Noorshams, Qais %A Rostami, Kiana %A Kounev, Samuel %A Reussner, Ralf %B Proceedings of the IEEE 22nd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems %D 2014 %T Modeling of I/O Performance Interference in Virtualized Environments with Queueing Petri Nets
• Parallel Simulation of Queueing Petri Nets. J. Walter; S. Spinner; S. Kounev; (2014, November).
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  @misc{talk-Walter-SOSP14, author = {Walter, J{\"u}rgen and Spinner, Simon and Kounev, Samuel}, howpublished = {Technical Talk at Symposion on Software Performance (SOSP) 2014}, keywords = {QPN}, month = 11, title = {{Parallel Simulation of Queueing Petri Nets}}, year = 2014 }

  %0 Generic %1 talk-Walter-SOSP14 %A Walter, Jürgen %A Spinner, Simon %A Kounev, Samuel %D 2014 %T Parallel Simulation of Queueing Petri Nets
• Data Center Network Throughput Analysis using Queueing Petri Nets. P. Rygielski; S. Kounev; in 34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014) (2014). 100–105.
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  In this paper, we address the problem of performance analysis in computer networks. We present a metamodel designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present the transformation to Queueing Petri Nets (QPNs) and simulate them using SimQPN simulator. We show that despite the high level of abstraction of the QPNs, we are able to estimate the utilization of resources with good accuracy within a short time.

  @inproceedings{RyKo2014-DCPerf-DNI2QPN, abstract = {In this paper, we address the problem of performance analysis in computer networks. We present a metamodel designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present the transformation to Queueing Petri Nets (QPNs) and simulate them using SimQPN simulator. We show that despite the high level of abstraction of the QPNs, we are able to estimate the utilization of resources with good accuracy within a short time.}, author = {Rygielski, Piotr and Kounev, Samuel}, booktitle = {34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014)}, keywords = {DNI}, month = {06}, pages = {100–105}, title = {{Data Center Network Throughput Analysis using Queueing Petri Nets}}, year = 2014 }

  %0 Conference Paper %1 RyKo2014-DCPerf-DNI2QPN %A Rygielski, Piotr %A Kounev, Samuel %B 34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, (DCPerf 2014) %D 2014 %P 100--105 %T Data Center Network Throughput Analysis using Queueing Petri Nets %X In this paper, we address the problem of performance analysis in computer networks. We present a metamodel designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present the transformation to Queueing Petri Nets (QPNs) and simulate them using SimQPN simulator. We show that despite the high level of abstraction of the QPNs, we are able to estimate the utilization of resources with good accuracy within a short time.

• Parallel Simulation of Queueing Petri Net Models. J. Walter; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2013, Oktober).
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  For years the CPU clock frequency was the key to improve processor performance. Nowadays, modern processors enable performance improvements by increasing the number of cores. However, existing software needs to be adapted to be able to utilize multiple cores. Such an adaptation poses many challenges in the field of discrete-event software simulation. Decades of intensive research have been spent to find a general solution for parallel discrete event simulation. In this context, QNs and PNs have been extensively studied. However, to the best of our knowledge, there is only one previous work that considers the concurrent simulation of QPN [Juergens1997]. This work focuses on comparing different synchronization algorithms and excludes a majority of lookahead calculation and net decomposition. In this thesis, we build upon and extend this work. For this purpose, we adapted and extended findings from QNs, PNs and parallel simulation in general. We apply our findings to SimQPN, which is a sequential simulation engine for QPN. Among other application areas, SimQPN is currently applied to online performance prediction for which a speedup due to parallelization is desirable. We present a parallel SimQPN implementation that employs application level and event level parallelism. A validation ensures the functional correctness of the new parallel implementations. The parallelization of multiple runs enables almost linear speedup. We parallelized the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.

  @mastersthesis{Walter2013-parallel-simulation, abstract = {For years the CPU clock frequency was the key to improve processor performance. Nowadays, modern processors enable performance improvements by increasing the number of cores. However, existing software needs to be adapted to be able to utilize multiple cores. Such an adaptation poses many challenges in the field of discrete-event software simulation. Decades of intensive research have been spent to find a general solution for parallel discrete event simulation. In this context, QNs and PNs have been extensively studied. However, to the best of our knowledge, there is only one previous work that considers the concurrent simulation of QPN [Juergens1997]. This work focuses on comparing different synchronization algorithms and excludes a majority of lookahead calculation and net decomposition. In this thesis, we build upon and extend this work. For this purpose, we adapted and extended findings from QNs, PNs and parallel simulation in general. We apply our findings to SimQPN, which is a sequential simulation engine for QPN. Among other application areas, SimQPN is currently applied to online performance prediction for which a speedup due to parallelization is desirable. We present a parallel SimQPN implementation that employs application level and event level parallelism. A validation ensures the functional correctness of the new parallel implementations. The parallelization of multiple runs enables almost linear speedup. We parallelized the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {Walter, J{\"u}rgen}, keywords = {QPN}, month = 10, school = {Karlsruhe Institute of Technology (KIT)}, title = {{Parallel Simulation of Queueing Petri Net Models}}, type = {Master Thesis}, year = 2013 }

  %0 Thesis %1 Walter2013-parallel-simulation %A Walter, Jürgen %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2013 %T Parallel Simulation of Queueing Petri Net Models %X For years the CPU clock frequency was the key to improve processor performance. Nowadays, modern processors enable performance improvements by increasing the number of cores. However, existing software needs to be adapted to be able to utilize multiple cores. Such an adaptation poses many challenges in the field of discrete-event software simulation. Decades of intensive research have been spent to find a general solution for parallel discrete event simulation. In this context, QNs and PNs have been extensively studied. However, to the best of our knowledge, there is only one previous work that considers the concurrent simulation of QPN [Juergens1997]. This work focuses on comparing different synchronization algorithms and excludes a majority of lookahead calculation and net decomposition. In this thesis, we build upon and extend this work. For this purpose, we adapted and extended findings from QNs, PNs and parallel simulation in general. We apply our findings to SimQPN, which is a sequential simulation engine for QPN. Among other application areas, SimQPN is currently applied to online performance prediction for which a speedup due to parallelization is desirable. We present a parallel SimQPN implementation that employs application level and event level parallelism. A validation ensures the functional correctness of the new parallel implementations. The parallelization of multiple runs enables almost linear speedup. We parallelized the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.

• Introduction to Queueing Petri Nets: Modeling Formalism, Tool Support and Case Studies (Tutorial Paper). S. Kounev; S. Spinner; P. Meier; in Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering (ICPE 2012), Boston, USA, 22-25 April 2012 (2012). 9–18.
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  Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and evaluating their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. This tutorial presents an introduction to queueing Petri nets first introducing the modeling formalism itself and then summarizing the results of several modeling case studies which demonstrate how queueing Petri nets can be used for performance modeling and analysis. As part of the tutorial, we present QPME (Queueing Petri net Modeling Environment), an open-source tool for stochastic modeling and analysis of systems using queueing Petri nets. Finally, we briefly present a model-to-model transformation automatically generating a queueing Petri net model from a higher-level software architecture model annotated with performance relevant information.

  @inproceedings{KoSpMe2012-icpe-QPME_Tutorial, abstract = {Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and evaluating their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. This tutorial presents an introduction to queueing Petri nets first introducing the modeling formalism itself and then summarizing the results of several modeling case studies which demonstrate how queueing Petri nets can be used for performance modeling and analysis. As part of the tutorial, we present QPME (Queueing Petri net Modeling Environment), an open-source tool for stochastic modeling and analysis of systems using queueing Petri nets. Finally, we briefly present a model-to-model transformation automatically generating a queueing Petri net model from a higher-level software architecture model annotated with performance relevant information.}, address = {New York, NY, USA}, author = {Kounev, Samuel and Spinner, Simon and Meier, Philipp}, booktitle = {Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering (ICPE 2012), Boston, USA, 22-25 April 2012}, keywords = {QPN}, month = {04}, pages = {9–18}, publisher = {ACM}, title = {Introduction to Queueing Petri Nets: Modeling Formalism, Tool Support and Case Studies (Tutorial Paper)}, year = 2012 }

  %0 Conference Paper %1 KoSpMe2012-icpe-QPME_Tutorial %A Kounev, Samuel %A Spinner, Simon %A Meier, Philipp %B Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering (ICPE 2012), Boston, USA, 22-25 April 2012 %C New York, NY, USA %D 2012 %I ACM %P 9--18 %T Introduction to Queueing Petri Nets: Modeling Formalism, Tool Support and Case Studies (Tutorial Paper) %X Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and evaluating their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. This tutorial presents an introduction to queueing Petri nets first introducing the modeling formalism itself and then summarizing the results of several modeling case studies which demonstrate how queueing Petri nets can be used for performance modeling and analysis. As part of the tutorial, we present QPME (Queueing Petri net Modeling Environment), an open-source tool for stochastic modeling and analysis of systems using queueing Petri nets. Finally, we briefly present a model-to-model transformation automatically generating a queueing Petri net model from a higher-level software architecture model annotated with performance relevant information.
• Stochastic Modeling and Analysis using QPME: Queueing Petri Net Modeling Environment v2.0. S. Spinner; S. Kounev; P. Meier; in Proceedings of the 33rd International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2012), S. Haddad, L. Pomello (Hrsg.) (2012). (Bd. 7347) 388–397.
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  Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present our tool QPME (Queueing Petri net Modeling Environment) for modeling and analysis using queueing Petri nets. QPME provides an Eclipse-based editor for building queueing Petri net models and a powerful simulation engine for analyzing these models. The development of the tool started in 2003 and since then the tool has been distributed to more than 120 organizations worldwide.

  @inproceedings{SpKoMe2012-PETRINETS-QPME, abstract = {Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present our tool QPME (Queueing Petri net Modeling Environment) for modeling and analysis using queueing Petri nets. QPME provides an Eclipse-based editor for building queueing Petri net models and a powerful simulation engine for analyzing these models. The development of the tool started in 2003 and since then the tool has been distributed to more than 120 organizations worldwide.}, address = {Berlin, Heidelberg}, author = {Spinner, Simon and Kounev, Samuel and Meier, Philipp}, booktitle = {Proceedings of the 33rd International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2012)}, editor = {Haddad, Serge and Pomello, Lucia}, keywords = {QPN}, month = {06}, pages = {388–397}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science (LNCS)}, title = {{Stochastic Modeling and Analysis using QPME: Queueing Petri Net Modeling Environment v2.0}}, volume = 7347, year = 2012 }

  %0 Conference Paper %1 SpKoMe2012-PETRINETS-QPME %A Spinner, Simon %A Kounev, Samuel %A Meier, Philipp %B Proceedings of the 33rd International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2012) %C Berlin, Heidelberg %D 2012 %E Haddad, Serge %E Pomello, Lucia %I Springer-Verlag %P 388--397 %T Stochastic Modeling and Analysis using QPME: Queueing Petri Net Modeling Environment v2.0 %U http://dx.doi.org/10.1007/978-3-642-31131-4_21 %V 7347 %X Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present our tool QPME (Queueing Petri net Modeling Environment) for modeling and analysis using queueing Petri nets. QPME provides an Eclipse-based editor for building queueing Petri net models and a powerful simulation engine for analyzing these models. The development of the tool started in 2003 and since then the tool has been distributed to more than 120 organizations worldwide.

• QPME 2.0 User’s Guide S. Kounev; S. Spinner; J. Walter; (2011). Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany.
  • [ BibTeX ]
  • [ EndNote ]
  @manual{KoSp2011-QPME20-UserGuide, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {Kounev, Samuel and Spinner, Simon and Walter, J{\"u}rgen}, keywords = {QPN}, month = {05}, organization = {Karlsruhe Institute of Technology}, title = {QPME 2.0 User's Guide}, year = 2011 }

  %0 Generic %1 KoSp2011-QPME20-UserGuide %A Kounev, Samuel %A Spinner, Simon %A Walter, Jürgen %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2011 %T QPME 2.0 User's Guide
• Automated Transformation of Component-based Software Architecture Models to Queueing Petri Nets. P. Meier; S. Kounev; H. Koziolek; in 19th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2011), Singapore, July 25--27 (2011).
  • [ BibTeX ]
  • [ EndNote ]
  @inproceedings{MeKoKo2011-MASCOTS-PCMtoQPN, author = {Meier, Philipp and Kounev, Samuel and Koziolek, Heiko}, booktitle = {19th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2011), Singapore, July 25–27}, keywords = {QPN}, title = {Automated Transformation of Component-based Software Architecture Models to Queueing Petri Nets}, year = 2011 }

  %0 Conference Paper %1 MeKoKo2011-MASCOTS-PCMtoQPN %A Meier, Philipp %A Kounev, Samuel %A Koziolek, Heiko %B 19th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2011), Singapore, July 25--27 %D 2011 %T Automated Transformation of Component-based Software Architecture Models to Queueing Petri Nets

• QPME 2.0 - A Tool for Stochastic Modeling and Analysis Using Queueing Petri Nets. S. Kounev; S. Spinner; P. Meier; in From Active Data Management to Event-Based Systems and More, K. Sachs, I. Petrov, P. Guerrero (Hrsg.) (2010). (Bd. 6462) 293–311.
  • [ BibTeX ]
  • [ EndNote ]
  @incollection{KoSpMe2010-Festschrift-QPME2, address = {Berlin, Heidelberg}, author = {Kounev, Samuel and Spinner, Simon and Meier, Philipp}, booktitle = {From Active Data Management to Event-Based Systems and More}, editor = {Sachs, Kai and Petrov, Ilia and Guerrero, Pablo}, keywords = {QPN}, pages = {293–311}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science}, title = {{QPME 2.0 - A Tool for Stochastic Modeling and Analysis Using Queueing Petri Nets}}, volume = 6462, year = 2010 }

  %0 Book Section %1 KoSpMe2010-Festschrift-QPME2 %A Kounev, Samuel %A Spinner, Simon %A Meier, Philipp %B From Active Data Management to Event-Based Systems and More %C Berlin, Heidelberg %D 2010 %E Sachs, Kai %E Petrov, Ilia %E Guerrero, Pablo %I Springer-Verlag %P 293--311 %T QPME 2.0 - A Tool for Stochastic Modeling and Analysis Using Queueing Petri Nets %V 6462

• QPME - A Performance Modeling Tool Based on Queueing Petri Nets. S. Kounev; C. Dutz; in ACM SIGMETRICS Performance Evaluation Review (PER), Special Issue on Tools for Computer Performance Modeling and Reliability Analysis (2009). 36 46–51.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present QPME (Queueing Petri net Modeling Environment) - a tool that supports the modeling and analysis of systems using queueing Petri nets. QPME provides an Eclipse-based editor for designing queueing Petri net models and a powerful simulation engine for analyzing the models. After presenting the tool, we discuss the ongoing work on the QPME project and the planned future enhancements of the tool.

  @article{KoDu2009-SIGMETRICS_PER-QPME, abstract = {Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present QPME (Queueing Petri net Modeling Environment) - a tool that supports the modeling and analysis of systems using queueing Petri nets. QPME provides an Eclipse-based editor for designing queueing Petri net models and a powerful simulation engine for analyzing the models. After presenting the tool, we discuss the ongoing work on the QPME project and the planned future enhancements of the tool.}, author = {Kounev, Samuel and Dutz, Christofer}, journal = {ACM SIGMETRICS Performance Evaluation Review (PER), Special Issue on Tools for Computer Performance Modeling and Reliability Analysis}, keywords = {QPN}, month = {03}, pages = {46–51}, publisher = {ACM}, title = {{QPME - A Performance Modeling Tool Based on Queueing Petri Nets}}, volume = 36, year = 2009 }

  %0 Journal Article %1 KoDu2009-SIGMETRICS_PER-QPME %A Kounev, Samuel %A Dutz, Christofer %D 2009 %I ACM %J ACM SIGMETRICS Performance Evaluation Review (PER), Special Issue on Tools for Computer Performance Modeling and Reliability Analysis %P 46--51 %T QPME - A Performance Modeling Tool Based on Queueing Petri Nets %V 36 %X Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present QPME (Queueing Petri net Modeling Environment) - a tool that supports the modeling and analysis of systems using queueing Petri nets. QPME provides an Eclipse-based editor for designing queueing Petri net models and a powerful simulation engine for analyzing the models. After presenting the tool, we discuss the ongoing work on the QPME project and the planned future enhancements of the tool.

• QPME (Queueing Petri net Modeling Environment) Homepage. S. Kounev; (2008).
  • [ BibTeX ]
  • [ EndNote ]
  • [ URL ]
  @misc{kounev2008a, author = {Kounev, Samuel}, howpublished = {{Online, \url{http://descartes.ipd.kit.edu/projects/QPME}}}, keywords = {QPN}, title = {{QPME (Queueing Petri net Modeling Environment) Homepage}}, year = 2008 }

  %0 Generic %1 kounev2008a %A Kounev, Samuel %D 2008 %T QPME (Queueing Petri net Modeling Environment) Homepage %U http://descartes.ipd.kit.edu/projects/QPME

• QPME 1.0 User’s Guide S. Kounev; C. Dutz; (2007). Technische Universität Darmstadt, Darmstadt, Germany.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  This document describes the software package QPME (Queueing Petri net Modeling Environment), a performance modeling and analysis tool based on the Queueing Petri Net (QPN) modeling formalism. QPN models are more sophisticated than conventional queueing networks and stochastic Petri nets and have greater expressive power. This provides a number of important benefits since it makes it possible to model systems at a higher degree of accuracy. QPME is made of two components: QPE (QPN Editor) and SimQPN (Simulator for QPNs). QPE provides a user-friendly graphical tool for modeling using QPNs based on the Eclipse/GEF framework. SimQPN provides an efficient discrete-event simulation engine for QPNs that makes it possible to analyze models of realistically-sized systems. QPME runs on a wide range of platforms including Windows, Linux and Solaris. QPME is developed and maintained by Samuel Kounev and Christofer Dutz.

  @manual{Ko2007-TUD-QPME_UsersGuide, abstract = {This document describes the software package QPME (Queueing Petri net Modeling Environment), a performance modeling and analysis tool based on the Queueing Petri Net (QPN) modeling formalism. QPN models are more sophisticated than conventional queueing networks and stochastic Petri nets and have greater expressive power. This provides a number of important benefits since it makes it possible to model systems at a higher degree of accuracy. QPME is made of two components: QPE (QPN Editor) and SimQPN (Simulator for QPNs). QPE provides a user-friendly graphical tool for modeling using QPNs based on the Eclipse/GEF framework. SimQPN provides an efficient discrete-event simulation engine for QPNs that makes it possible to analyze models of realistically-sized systems. QPME runs on a wide range of platforms including Windows, Linux and Solaris. QPME is developed and maintained by Samuel Kounev and Christofer Dutz.}, address = {Darmstadt, Germany}, author = {Kounev, Samuel and Dutz, Christofer}, keywords = {QPN}, month = {01}, organization = {Technische Universit{{\"a}}t Darmstadt}, title = {{QPME 1.0 User's Guide}}, year = 2007 }

  %0 Generic %1 Ko2007-TUD-QPME_UsersGuide %A Kounev, Samuel %A Dutz, Christofer %C Darmstadt, Germany %D 2007 %T QPME 1.0 User's Guide %X This document describes the software package QPME (Queueing Petri net Modeling Environment), a performance modeling and analysis tool based on the Queueing Petri Net (QPN) modeling formalism. QPN models are more sophisticated than conventional queueing networks and stochastic Petri nets and have greater expressive power. This provides a number of important benefits since it makes it possible to model systems at a higher degree of accuracy. QPME is made of two components: QPE (QPN Editor) and SimQPN (Simulator for QPNs). QPE provides a user-friendly graphical tool for modeling using QPNs based on the Eclipse/GEF framework. SimQPN provides an efficient discrete-event simulation engine for QPNs that makes it possible to analyze models of realistically-sized systems. QPME runs on a wide range of platforms including Windows, Linux and Solaris. QPME is developed and maintained by Samuel Kounev and Christofer Dutz.

• QPME - Queueing Petri Net Modeling Environment. S. Kounev; C. Dutz; A. Buchmann; in Proceedings of the 3rd International Conference on Quantitative Evaluation of SysTems (QEST 2006), Riverside, California, USA, September 11-14, 2006 (2006). 115–116.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. However, currently available tools for modeling and analysis using queueing Petri nets are very limited in terms of the scalability of the analysis algorithms they provide. Moreover, tools are available only on highly specialized platforms unaccessible to most potential users. In this paper, we present QPME - a Queueing Petri Net Modeling Environment that supports the modeling and analysis of systems using queueing Petri nets. QPME runs on a wide range of platforms and provides a powerful simulation engine that can be used to analyze models of realistically-sized systems.

  @inproceedings{KoDuBu2006-QEST-QPME, abstract = {Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. However, currently available tools for modeling and analysis using queueing Petri nets are very limited in terms of the scalability of the analysis algorithms they provide. Moreover, tools are available only on highly specialized platforms unaccessible to most potential users. In this paper, we present QPME - a Queueing Petri Net Modeling Environment that supports the modeling and analysis of systems using queueing Petri nets. QPME runs on a wide range of platforms and provides a powerful simulation engine that can be used to analyze models of realistically-sized systems.}, address = {Washington, DC, USA}, author = {Kounev, Samuel and Dutz, Christofer and Buchmann, Alejandro}, booktitle = {Proceedings of the 3rd International Conference on Quantitative Evaluation of SysTems (QEST 2006), Riverside, California, USA, September 11-14, 2006}, keywords = {QPN}, month = {09}, pages = {115–116}, publisher = {IEEE Computer Society}, title = {{QPME - Queueing Petri Net Modeling Environment}}, year = 2006 }

  %0 Conference Paper %1 KoDuBu2006-QEST-QPME %A Kounev, Samuel %A Dutz, Christofer %A Buchmann, Alejandro %B Proceedings of the 3rd International Conference on Quantitative Evaluation of SysTems (QEST 2006), Riverside, California, USA, September 11-14, 2006 %C Washington, DC, USA %D 2006 %I IEEE Computer Society %P 115--116 %T QPME - Queueing Petri Net Modeling Environment %X Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. However, currently available tools for modeling and analysis using queueing Petri nets are very limited in terms of the scalability of the analysis algorithms they provide. Moreover, tools are available only on highly specialized platforms unaccessible to most potential users. In this paper, we present QPME - a Queueing Petri Net Modeling Environment that supports the modeling and analysis of systems using queueing Petri nets. QPME runs on a wide range of platforms and provides a powerful simulation engine that can be used to analyze models of realistically-sized systems.
• SimQPN - a tool and methodology for analyzing queueing Petri net models by means of simulation. S. Kounev; A. Buchmann; in Performance Evaluation (2006). 63(4-5) 364–394.
  • [ BibTeX ]
  • [ EndNote ]
  @article{KoBu2006-PERFEVAL-SimQPN, address = {Amsterdam, The Netherlands}, author = {Kounev, Samuel and Buchmann, Alejandro}, journal = {Performance Evaluation}, keywords = {QPN}, month = {05}, number = {4-5}, pages = {364–394}, publisher = {Elsevier Science Publishers B. V.}, title = {{SimQPN - a tool and methodology for analyzing queueing Petri net models by means of simulation}}, volume = 63, year = 2006 }

  %0 Journal Article %1 KoBu2006-PERFEVAL-SimQPN %A Kounev, Samuel %A Buchmann, Alejandro %C Amsterdam, The Netherlands %D 2006 %I Elsevier Science Publishers B. V. %J Performance Evaluation %N 4-5 %P 364--394 %T SimQPN - a tool and methodology for analyzing queueing Petri net models by means of simulation %V 63

• Performance Engineering of Distributed Component-Based Systems - Benchmarking, Modeling and Performance Prediction. S. Kounev; Thesis; Technische Universität Darmstadt, Germany. (2005, Dezember).
  • [ BibTeX ]
  • [ EndNote ]
  @phdthesis{Ko2005-TUD-PhD_Thesis_as_phdthesis_class, author = {Kounev, Samuel}, keywords = {SPEC}, month = 12, note = {<b>Distinguished Dissertation Award</b>}, school = {Technische Universit{\"a}t Darmstadt, Germany}, title = {Performance Engineering of Distributed Component-Based Systems - Benchmarking, Modeling and Performance Prediction}, year = 2005 }

  %0 Thesis %1 Ko2005-TUD-PhD_Thesis_as_phdthesis_class %A Kounev, Samuel %D 2005 %T Performance Engineering of Distributed Component-Based Systems - Benchmarking, Modeling and Performance Prediction
• Performance Engineering of Distributed Component-Based Systems - Benchmarking, Modeling and Performance Prediction S. Kounev; (2005). Shaker Verlag, Ph.D. Thesis, Technische Universität Darmstadt, Germany.
  • [ BibTeX ]
  • [ EndNote ]
  @book{Ko2005-TUD-PhD_Thesis, author = {Kounev, Samuel}, keywords = {QPME_Bibliography}, month = 12, note = {<b>Distinguished Dissertation Award</b>}, publisher = {Shaker Verlag, Ph.D. Thesis, Technische Universit{\"a}t Darmstadt, Germany}, title = {Performance Engineering of Distributed Component-Based Systems - Benchmarking, Modeling and Performance Prediction}, year = 2005 }

  %0 Book %1 Ko2005-TUD-PhD_Thesis %A Kounev, Samuel %D 2005 %I Shaker Verlag, Ph.D. Thesis, Technische Universität Darmstadt, Germany %T Performance Engineering of Distributed Component-Based Systems - Benchmarking, Modeling and Performance Prediction