Simon Eismann

Serverless Applications:Why, When, and How?. Eismann, Simon; Joel, Scheuner; van Eyk, Erwin; Schwinger, Maximilian; Grohmann, Johannes; Herbst, Nikolas; Abad, Cristina; Iosup, Alexandru; in IEEE Software (2021). 38(1) 32–39.
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Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing—the most extensive study to date.
@article{eismann2020serverless, abstract = {Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing—the most extensive study to date.}, author = {Eismann, Simon and Joel, Scheuner and van Eyk, Erwin and Schwinger, Maximilian and Grohmann, Johannes and Herbst, Nikolas and Abad, Cristina and Iosup, Alexandru}, journal = {IEEE Software}, keywords = {myown t_journalmagazine descartes select}, number = 1, pages = {32–39}, title = {Serverless Applications:Why, When, and How?}, volume = 38, year = 2021 }
Microservices: A Performance Tester’s Dream or Nightmare?. Eismann, Simon; Bezemer, Cor-Paul; Shang, Weiyi; Okanovic, Dusan; van Hoorn, Andre; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020).
Acceptance Rate: 23.4% (15/64)
[ Abstract ] [ BibTeX ] [ Download ]
 
In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service. On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications. In this paper, we discuss the benefits and challenges of microservices from a performance testers point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.
@inproceedings{EiBeShOkHo2020-ICPE-ServerlessWorkflows, abstract = {In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service. On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications. In this paper, we discuss the benefits and challenges of microservices from a performance testers point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.}, author = {Eismann, Simon and Bezemer, Cor-Paul and Shang, Weiyi and Okanovic, Dusan and van Hoorn, Andre}, booktitle = {Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {descartes myown se2 select t_full}, month = {April}, note = {{Acceptance Rate: 23.4% (15/64)}}, series = {ICPE'20}, title = {{Microservices: A Performance Tester's Dream or Nightmare?}}, year = 2020 }
Predicting the Costs of Serverless Workflows. Eismann, Simon; Grohmann, Johannes; van Eyk, Erwin; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE) (2020). 265–276.
Acceptance Rate: 23.4% (15/64)
[ Abstract ] [ BibTeX ] [ URL ] [ Download ]
 
Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.
@inproceedings{EiGrEyHeKo2020-ICPE-ServerlessWorkflows, abstract = {Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.}, address = {New York, NY, USA}, author = {Eismann, Simon and Grohmann, Johannes and van Eyk, Erwin and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {t_full se2 myown descartes select}, month = {April}, note = {{Acceptance Rate: 23.4% (15/64)}}, pages = {265–276}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '20}, title = {Predicting the Costs of Serverless Workflows}, year = 2020 }
Integrating Statistical Response Time Models in Architectural Performance Models. Eismann, Simon; Grohmann, Johannes; Walter, Jürgen; von Kistowski, Jóakim; Kounev, Samuel; in Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA) (2019). 71–80.
Acceptance Rate: 21,9% (21/96)
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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{ü}rgen and von Kistowski, J{ó}akim and Kounev, Samuel}, booktitle = {Proceedings of the 2019 IEEE International Conference on Software Architecture (ICSA)}, keywords = {DML Meta-models Prediction t_full Statistical_estimation_and_machine_learning QPN Formal_architecture_modeling Simulation QPME PRISMA myown Performance descartes select Analytical_and_simulation-based_analysis}, month = {March}, note = {Acceptance Rate: 21,9\% (21/96)}, pages = {71–80}, publisher = {IEEE}, title = {{Integrating Statistical Response Time Models in Architectural Performance Models}}, year = 2019 }
Modeling of Parametric Dependencies for Performance Prediction of Component-based Software Systems at Run-time. Eismann, Simon; Walter, Jürgen; von Kistowski, Jóakim; Kounev, Samuel; in 2018 IEEE International Conference on Software Architecture (ICSA) (2018). 135–144.
Acceptance Rate: 25,6% (22/86)
[ Abstract ] [ BibTeX ] [ Download ]
 
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{ü}rgen and von Kistowski, J{ó}akim and Kounev, Samuel}, booktitle = {2018 IEEE International Conference on Software Architecture (ICSA)}, keywords = {DML Formal_architecture_modeling Meta-models PRISMA Performance Prediction Resource_management descartes myown select t_full}, month = {April}, 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 }

2020

Predicting the Impact of Function Size in Serverless Computing. Bui, Long; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{bui2020predicting, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Bui, Long}, keywords = {Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann se2_mastersthesis t_studentthesis}, school = {University of W{ü}rzburg}, title = {Predicting the Impact of Function Size in Serverless Computing}, year = 2020 }
%0 Thesis %1 bui2020predicting %A Bui, Long %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Predicting the Impact of Function Size in Serverless Computing
Generating Statistical Response Time Models from Architectural Performance Models. Schaffarczyk, Kai; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{schaffarczyk2020generating, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Schaffarczyk, Kai}, keywords = {Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann se2_mastersthesis}, school = {University of W{ü}rzburg}, title = {Generating Statistical Response Time Models from Architectural Performance Models}, year = 2020 }
%0 Thesis %1 schaffarczyk2020generating %A Schaffarczyk, Kai %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Generating Statistical Response Time Models from Architectural Performance Models
Generating Performance-Aware Web Applications from BPMN Models. Hümmer, Simon; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{hummer2020generating, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hümmer, Simon}, keywords = {Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann se2_mastersthesis t_studentthesis}, school = {University of W{ü}rzburg}, title = {Generating Performance-Aware Web Applications from BPMN Models}, year = 2020 }
%0 Thesis %1 hummer2020generating %A Hümmer, Simon %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2020 %T Generating Performance-Aware Web Applications from BPMN Models

2019

Exploring the Limitations of Statistical Response Time Models in Architectural Performance Models. Trapp, Simon; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, Dezember).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Trapp2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Trapp, Simon}, keywords = {Thesis_supervised_by_Simon_Eismann descartes se2_mastersthesis}, month = {December}, school = {University of W{ü}rzburg}, title = {Exploring the {L}imitations of {S}tatistical {R}esponse {T}ime {M}odels in {A}rchitectural {P}erformance {M}odels}, type = {{Bachelor Thesis}}, year = 2019 }
%0 Thesis %1 Trapp2019 %A Trapp, Simon %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Exploring the Limitations of Statistical Response Time Models in Architectural Performance Models
Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen. Hick, Lars; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, März).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Hick2019, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Hick, Lars}, keywords = {Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann descartes se2_mastersthesis}, month = {February}, school = {University of W{ü}rzburg}, title = {{Erweiterung von Komponenten-basierten Performance Modellen f{ü}r Micro-service Architekturen}}, type = {{Bachelor Thesis}}, year = 2019 }
%0 Thesis %1 Hick2019 %A Hick, Lars %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen

2018

Blackbox Learning of Parametric Dependencies for Performance Models from Monitoring Data. Ackermann, Vanessa; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, März).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Ackermann2018, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Ackermann, Vanessa}, keywords = {Thesis_supervised_by_Johannes_Grohmann Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann descartes se2_mastersthesis t_studentthesis}, month = {March}, school = {University of W{ü}rzburg}, title = {{Blackbox Learning of Parametric Dependencies for Performance Models from Monitoring Data}}, type = {{Bachelor Thesis}}, year = 2018 }
%0 Thesis %1 Ackermann2018 %A Ackermann, Vanessa %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2018 %T Blackbox Learning of Parametric Dependencies for Performance Models from Monitoring Data
Automatic Identification of Parametric Dependencies for Performance Models. Elflein, Sven; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{Elflein2018, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Elflein, Sven}, keywords = {Thesis_supervised_by_Johannes_Grohmann Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Eismann descartes se2_mastersthesis t_studentthesis}, month = {September}, note = {<b>W{ü}rzburg Software Engineering Award sponsored by Bosch Rexroth</b>}, school = {University of W{ü}rzburg}, title = {{Automatic Identification of Parametric Dependencies for Performance Models}}, type = {{Bachelor Thesis}}, year = 2018 }
%0 Thesis %1 Elflein2018 %A Elflein, Sven %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2018 %T Automatic Identification of Parametric Dependencies for Performance Models