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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, and J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
4 years ago by @samuel.kounev
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, and J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
4 years ago by @simon.eismann
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, and J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
4 years ago by @se-group
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, and J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
4 years ago by @joh.grohmann
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3Incremental Calibration of Architectural Performance Models with Parametric Dependencies
M. Mazkatli, D. Monschein, J. Grohmann, and A. Koziolek. 2020 IEEE International Conference on Software Architecture (ICSA 2020), page 23--34. IEEE, (March 2020)
4 years ago by @se-group
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3Incremental Calibration of Architectural Performance Models with Parametric Dependencies
M. Mazkatli, D. Monschein, J. Grohmann, and A. Koziolek. 2020 IEEE International Conference on Software Architecture (ICSA 2020), page 23--34. IEEE, (March 2020)
5 years ago by @joh.grohmann
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3Dynamic Hybrid Forecasting for Self-Aware Systems
M. Züfle. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (October 2017)Würzburg Software Engineering Award sponsored by infosim.
5 years ago by @andre.bauer
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Automated_model_learningChameleonOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-aware-computingSupervised_by_Nikolas_HerbstTelescopeThesis_supervised_by_Andre_BauerThesis_supervised_by_SE_memberdescartesse2_mastersthesist_studentthesis
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1Modeling Server Energy Consumption using Machine Learning
T. Oestreicher. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (October 2016)
5 years ago by @se-group
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Tool
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3Dynamic Hybrid Forecasting for Self-Aware Systems
M. Züfle. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (October 2017)Würzburg Software Engineering Award sponsored by infosim.
5 years ago by @se-group
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Automated_model_learning
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Online_monitoring_and_forecasting
Optimization
Performance
Prediction
Self-aware-computing
Supervised_by_Nikolas_Herbst
Telescope
Thesis_supervised_by_Andre_Bauer
Thesis_supervised_by_SE_member
descartes
se2_mastersthesis
t_studentthesis
Automated_model_learningChameleonOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-aware-computingSupervised_by_Nikolas_HerbstTelescopeThesis_supervised_by_Andre_BauerThesis_supervised_by_SE_memberdescartesse2_mastersthesist_studentthesis
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3Dynamic Hybrid Forecasting for Self-Aware Systems
M. Züfle. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (October 2017)Würzburg Software Engineering Award sponsored by infosim.
5 years ago by @marwin.zuefle
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Chameleon
Online_monitoring_and_forecasting
Optimization
Performance
Prediction
Self-aware-computing
Supervised_by_Nikolas_Herbst
Telescope
Thesis_supervised_by_Andre_Bauer
Thesis_supervised_by_SE_member
descartes
myown
se2_mastersthesis
t_studentthesis
Automated_model_learningChameleonOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-aware-computingSupervised_by_Nikolas_HerbstTelescopeThesis_supervised_by_Andre_BauerThesis_supervised_by_SE_memberdescartesmyownse2_mastersthesist_studentthesis
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1Automated Extraction of Palladio Component Models from Running Enterprise Java Applications
F. Brosig. Universität Karlsruhe (TH), Karlsruhe, Germany, (June 2009)FZI Prize "Best Diploma Thesis".
5 years ago by @se-group
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Award
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Automated_model_learningAwardInstrumentation_profiling_and_workload_characterizationPerformancePredictionThesis_supervised_by_SE_memberdescartesse2_mastersthesist_studentthesis
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2Flexible Performance Predictions at Run-time
S. Eismann. HotCloudperf 2018, (April 2018)
5 years ago by @se-group
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DMLStatistical_estimation_and_machine_learningse2_talkPerformanceSimulationt_talkPredictiondescartesAutomated_model_learningMeta-modelsAnalytical_and_simulation-based_analysis
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1Performance Evaluation: Metrics, Models and Benchmarks, Proceedings of the 2008 SPEC International Performance Evaluation Workshop (SIPEW 2008), Darmstadt, Germany, June 27-28
K. Sachs. volume 5119 of Lecture Notes in Computer Science (LNCS), Heidelberg, Germany, Springer, (June 2008)
5 years ago by @se-group
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Application_quality_of_service_management
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Online_monitoring_and_forecasting
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Performance
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Resource_management
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Analytical_and_simulation-based_analysisApplication_quality_of_service_managementAutomated_model_learningFormal_architecture_modelingInstrumentation_profiling_and_workload_characterizationMeta-modelsMetrics_and_benchmarking_methodologiesOnline_monitoring_and_forecastingOptimizationPerformancePowerPower-energy_efficient_computingPredictionResource_managementSPECSimulationt_workshop
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6On Learning Parametric Dependencies from Monitoring Data
J. Grohmann, S. Eismann, and S. Kounev. Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19), (November 2019)
5 years ago by @se-group
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6On Learning Parametric Dependencies from Monitoring Data
J. Grohmann, S. Eismann, and S. Kounev. Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19), (November 2019)
5 years ago by @samuel.kounev
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6On Learning Parametric Dependencies from Monitoring Data
J. Grohmann, S. Eismann, and S. Kounev. Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19), (November 2019)
5 years ago by @simon.eismann
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t_workshop
myown
Automated_model_learningPerformancePredictionSelf-adaptive-systemsSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_workshopmyown
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5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques
J. Grohmann, S. Eismann, S. Elflein, M. Mazkatli, J. von Kistowski, and S. Kounev. 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 309--322. IEEE Computer Society, (October 2019)Acceptance Rate: 23.8% (29/122).
5 years ago by @samuel.kounev
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Automated_model_learning
DML
PRISMA
Performance
Prediction
Self-aware-computing
Statistical_estimation_and_machine_learning
descartes
t_full
myown
Automated_model_learningDMLPRISMAPerformancePredictionSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_fullmyown
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5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques
J. Grohmann, S. Eismann, S. Elflein, M. Mazkatli, J. von Kistowski, and S. Kounev. 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 309--322. IEEE Computer Society, (October 2019)Acceptance Rate: 23.8% (29/122).
5 years ago by @se-group
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Automated_model_learning
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PRISMA
Performance
Prediction
Self-aware-computing
Statistical_estimation_and_machine_learning
descartes
t_full
Automated_model_learningDMLPRISMAPerformancePredictionSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_full
(0)
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5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques
J. Grohmann, S. Eismann, S. Elflein, M. Mazkatli, J. von Kistowski, and S. Kounev. 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), page 309--322. IEEE Computer Society, (October 2019)Acceptance Rate: 23.8% (29/122).
5 years ago by @simon.eismann
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Automated_model_learning
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PRISMA
Performance
Prediction
Self-aware-computing
Statistical_estimation_and_machine_learning
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t_full
myown
Automated_model_learningDMLPRISMAPerformancePredictionSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_fullmyown
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5The Vision of Self-Aware Performance Models
J. Grohmann, S. Eismann, and S. Kounev. 2018 IEEE International Conference on Software Architecture Companion (ICSA-C), page 60--63. (April 2018)
5 years ago by @se-group
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Analytical_and_simulation-based_analysisAutomated_model_learningDMLOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-adaptive-systemsSelf-aware-computingSimulationStatistical_estimation_and_machine_learningdescartest_visionposition
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

3Incremental Calibration of Architectural Performance Models with Parametric Dependencies

3Incremental Calibration of Architectural Performance Models with Parametric Dependencies

3Dynamic Hybrid Forecasting for Self-Aware Systems

1Modeling Server Energy Consumption using Machine Learning

3Dynamic Hybrid Forecasting for Self-Aware Systems

3Dynamic Hybrid Forecasting for Self-Aware Systems

1Automated Extraction of Palladio Component Models from Running Enterprise Java Applications

2Flexible Performance Predictions at Run-time

1Performance Evaluation: Metrics, Models and Benchmarks, Proceedings of the 2008 SPEC International Performance Evaluation Workshop (SIPEW 2008), Darmstadt, Germany, June 27-28

6On Learning Parametric Dependencies from Monitoring Data

6On Learning Parametric Dependencies from Monitoring Data

6On Learning Parametric Dependencies from Monitoring Data

5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques

5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques

5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques

5The Vision of Self-Aware Performance Models

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