Supervised student theses

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  {A Survey on Vehicle Traffic Forecasting} Kamga, Guy Borel Tabueu; Thesis; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2022, April).
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  @mastersthesis{kamga2022survey, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Kamga, Guy Borel Tabueu}, keywords = {thesis_supervised_by_marius.hadry}, month = {April}, title = {{A Survey on Vehicle Traffic Forecasting}}, type = {{Bachelor Thesis}}, year = 2022 }

  %0 Thesis %1 kamga2022survey %A Kamga, Guy Borel Tabueu %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2022 %T {A Survey on Vehicle Traffic Forecasting}
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  {A Survey on Dwell Time Prediction in Intelligent Transportation Systems} Nguepi, Mickayel Aime Nangue; Thesis; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2022, April).
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  @mastersthesis{nguepi2022survey, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Nguepi, Mickayel Aime Nangue}, keywords = {thesis_supervised_by_marius.hadry}, month = {April}, title = {{A Survey on Dwell Time Prediction in Intelligent Transportation Systems}}, type = {{Bachelor Thesis}}, year = 2022 }

  %0 Thesis %1 nguepi2022survey %A Nguepi, Mickayel Aime Nangue %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2022 %T {A Survey on Dwell Time Prediction in Intelligent Transportation Systems}

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  A Self-Learning Meta-Optimization Framework for Adaptation Planning Hadry, Marius; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2021, May).
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  @mastersthesis{hadry2021selflearning, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hadry, Marius}, keywords = {t_studentthesis}, month = {may}, school = {University of W{\"u}rzburg}, title = {A Self-Learning Meta-Optimization Framework for Adaptation Planning}, year = 2021 }

  %0 Thesis %1 hadry2021selflearning %A Hadry, Marius %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2021 %T A Self-Learning Meta-Optimization Framework for Adaptation Planning

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  Combining Platooning with Corridor Control Systems Johnson, Andre; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, November).
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  @mastersthesis{johnson2020combining, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Johnson, Andre}, keywords = {t_studentthesis}, month = {November}, school = {University of W{\"u}rzburg}, title = {Combining Platooning with Corridor Control Systems}, year = 2020 }

  %0 Thesis %1 johnson2020combining %A Johnson, Andre %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Combining Platooning with Corridor Control Systems
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  Situation-Aware Meta-Optimization of Platooning Coordination Strategies Hefter, Johannes; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, September).
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  @mastersthesis{hefter2020situationaware, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hefter, Johannes}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {Situation-Aware Meta-Optimization of Platooning Coordination Strategies}, year = 2020 }

  %0 Thesis %1 hefter2020situationaware %A Hefter, Johannes %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Situation-Aware Meta-Optimization of Platooning Coordination Strategies
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  Optimizing Storage Assignment and Order Picking in Mezzanine Warehouses using Nature-Inspired Algorithms Müller, Patrick; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, September).
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  @mastersthesis{muller2020optimizing, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Müller, Patrick}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {Optimizing Storage Assignment and Order Picking in Mezzanine Warehouses using Nature-Inspired Algorithms}, year = 2020 }

  %0 Thesis %1 muller2020optimizing %A Müller, Patrick %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Optimizing Storage Assignment and Order Picking in Mezzanine Warehouses using Nature-Inspired Algorithms
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  Minimizing Makespan in Flow Shop Scheduling Problems Using Optimization Algorithms Schleicher, Johannes; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, September).
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  @mastersthesis{schleicher2020minimizing, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Schleicher, Johannes}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {Minimizing Makespan in Flow Shop Scheduling Problems Using Optimization Algorithms}, year = 2020 }

  %0 Thesis %1 schleicher2020minimizing %A Schleicher, Johannes %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Minimizing Makespan in Flow Shop Scheduling Problems Using Optimization Algorithms
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  Optimal Platooning Coordination using Multiple Objectives Hoang Tran, Ha Giang; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, September).
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  @mastersthesis{hoangtran2020optimal, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hoang Tran, Ha Giang}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {Optimal Platooning Coordination using Multiple Objectives}, year = 2020 }

  %0 Thesis %1 hoangtran2020optimal %A Hoang Tran, Ha Giang %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Optimal Platooning Coordination using Multiple Objectives
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  Predicting Performance Degradations of Microservice Applications Straesser, Martin; Thesis; University of Wuerzburg. (2020, May).

  Wuerzburg Software Engineering Award 2020: Best Master Thesis
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  @mastersthesis{straesser2020predicting, author = {Straesser, Martin}, keywords = {t_studentthesis}, month = {may}, note = {Wuerzburg Software Engineering Award 2020: Best Master Thesis}, school = {University of Wuerzburg}, title = {Predicting Performance Degradations of Microservice Applications}, year = 2020 }

  %0 Thesis %1 straesser2020predicting %A Straesser, Martin %D 2020 %T Predicting Performance Degradations of Microservice Applications
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  Solving the Rich Vehicle Routing Problem with Bio-inspired Algorithms König, Maximilian; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, January).
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  @mastersthesis{konig2020solving, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {König, Maximilian}, keywords = {t_studentthesis}, month = {January}, school = {University of W{\"u}rzburg}, title = {Solving the Rich Vehicle Routing Problem with Bio-inspired Algorithms}, type = {{Master Thesis}}, year = 2020 }

  %0 Thesis %1 konig2020solving %A König, Maximilian %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Solving the Rich Vehicle Routing Problem with Bio-inspired Algorithms
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  Minimising Setup Times in Production Environments Using Nature-Inspired Optimisation Hartmann, Stephan; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2020, March).
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  @mastersthesis{hartmann2020minimising, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hartmann, Stephan}, keywords = {t_studentthesis}, month = {March}, school = {University of W{\"u}rzburg}, title = {Minimising Setup Times in Production Environments Using Nature-Inspired Optimisation}, type = {{Master Thesis}}, year = 2020 }

  %0 Thesis %1 hartmann2020minimising %A Hartmann, Stephan %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2020 %T Minimising Setup Times in Production Environments Using Nature-Inspired Optimisation

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  {Comparison of Compensation Mechanisms for Intra-Platoon Position Effects} Stubenrauch, Kevin; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, July).

  W{\"u}rzburg Software Engineering Award sponsored by Bosch Rexroth
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  @mastersthesis{Stubenrauch2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Stubenrauch, Kevin}, keywords = {t_studentthesis}, month = {July}, note = {<b>W{\"u}rzburg Software Engineering Award sponsored by Bosch Rexroth</b>}, school = {University of W{\"u}rzburg}, title = {{Comparison of Compensation Mechanisms for Intra-Platoon Position Effects}}, type = {{Bachelor Thesis}}, year = 2019 }

  %0 Thesis %1 Stubenrauch2019 %A Stubenrauch, Kevin %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T {Comparison of Compensation Mechanisms for Intra-Platoon Position Effects}
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  {Towards Self-Aware Multirotor Formations} Kaiser, Dennis; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, September).

  W{\"u}rzburg Software Engineering Award sponsored by infosim
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  @mastersthesis{Kaiser2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Kaiser, Dennis}, keywords = {t_studentthesis}, month = {September}, note = {<b>W{\"u}rzburg Software Engineering Award sponsored by infosim</b>}, school = {University of W{\"u}rzburg}, title = {{Towards Self-Aware Multirotor Formations}}, type = {{Master Thesis}}, year = 2019 }

  %0 Thesis %1 Kaiser2019 %A Kaiser, Dennis %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T {Towards Self-Aware Multirotor Formations}
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  {An Evaluation Tool for Cost-Aware Vehicle Route Calculation} Hadry, Marius; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, August).
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  @mastersthesis{Hadry2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hadry, Marius}, keywords = {t_studentthesis}, month = {August}, school = {University of W{\"u}rzburg}, title = {{An Evaluation Tool for Cost-Aware Vehicle Route Calculation}}, type = {{Bachelor Thesis}}, year = 2019 }

  %0 Thesis %1 Hadry2019 %A Hadry, Marius %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T {An Evaluation Tool for Cost-Aware Vehicle Route Calculation}
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  {Neukonzeptualisierung des Programmiervorkurses mit Fokus auf {\"U}bung und Vertiefung} Migendt, Christian; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, July).
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  @mastersthesis{Migendt2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Migendt, Christian}, keywords = {t_studentthesis}, month = {July}, school = {University of W{\"u}rzburg}, title = {{Neukonzeptualisierung des Programmiervorkurses mit Fokus auf {\"U}bung und Vertiefung}}, type = {{Bachelor Thesis}}, year = 2019 }

  %0 Thesis %1 Migendt2019 %A Migendt, Christian %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T {Neukonzeptualisierung des Programmiervorkurses mit Fokus auf {\"U}bung und Vertiefung}
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  {Analysis of Centralized Platooning Coordination Approaches} Dalhof, Florian; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, August).
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  @mastersthesis{Dalhof2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Dalhof, Florian}, keywords = {t_studentthesis}, month = {August}, school = {University of W{\"u}rzburg}, title = {{Analysis of Centralized Platooning Coordination Approaches}}, type = {{Bachelor Thesis}}, year = 2019 }

  %0 Thesis %1 Dalhof2019 %A Dalhof, Florian %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T {Analysis of Centralized Platooning Coordination Approaches}
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  Mosquitto Swatter: Demonstrating MQTT Vulnerabilities Metzler, Samuel; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, February).
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  @mastersthesis{Metzler2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Metzler, Samuel}, keywords = {t_studentthesis}, month = {February}, school = {University of W{\"u}rzburg}, title = {Mosquitto Swatter: Demonstrating MQTT Vulnerabilities}, type = {{Bachelor Thesis}}, year = 2019 }

  %0 Thesis %1 Metzler2019 %A Metzler, Samuel %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T Mosquitto Swatter: Demonstrating MQTT Vulnerabilities
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  Hypercall Interface Testing for the Example of Hyper-V Beierlieb, Lukas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, December).

  Würzburg Software Engineering Award Runner Up Masterthesis
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  @mastersthesis{beierlieb2019hypercall, address = {Am Hubland, Informatikgebäude, 97074 Würzburg, Germany}, author = {Beierlieb, Lukas}, keywords = {virtualization}, month = {december}, note = {Würzburg Software Engineering Award Runner Up Masterthesis}, school = {University of Würzburg}, title = {Hypercall Interface Testing for the Example of Hyper-V}, type = {Bachelor Thesis}, year = 2019 }

  %0 Thesis %1 beierlieb2019hypercall %A Beierlieb, Lukas %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2019 %T Hypercall Interface Testing for the Example of Hyper-V
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  Heat-aware and CPU Boost-oriented Server Load Rotation Knapp, Andreas; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, November).
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  @mastersthesis{Knapp2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Knapp, Andreas}, keywords = {t_studentthesis}, month = {November}, school = {University of W{\"u}rzburg}, title = {Heat-aware and CPU Boost-oriented Server Load Rotation}, type = {{Master Thesis}}, year = 2019 }

  %0 Thesis %1 Knapp2019 %A Knapp, Andreas %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T Heat-aware and CPU Boost-oriented Server Load Rotation
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  Review of Security Mechanisms in Publish/Subscribe Systems and their Applicability in IoT Context Geiger, Ariane; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2019, January).
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  @mastersthesis{Geiger2019, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Geiger, Ariane}, keywords = {t_studentthesis}, month = {January}, school = {University of W{\"u}rzburg}, title = {Review of Security Mechanisms in Publish/Subscribe Systems and their Applicability in IoT Context}, type = {{Bachelor Thesis}}, year = 2019 }

  %0 Thesis %1 Geiger2019 %A Geiger, Ariane %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2019 %T Review of Security Mechanisms in Publish/Subscribe Systems and their Applicability in IoT Context

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  Performance Evaluation of Security Network Function Reordering Fella, Nicolas; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, December).
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  @mastersthesis{Fella2018, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Fella, Nicolas}, keywords = {t_studentthesis}, month = {December}, school = {University of W{\"u}rzburg}, title = {Performance Evaluation of Security Network Function Reordering}, type = {{Bachelor Thesis}}, year = 2018 }

  %0 Thesis %1 Fella2018 %A Fella, Nicolas %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T Performance Evaluation of Security Network Function Reordering
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  DIMAQS - Dynamic Identification of Malicious Query Sequences Jobst, Michael; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, June).
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  @mastersthesis{Jobst2018, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Jobst, Michael}, keywords = {t_studentthesis}, month = {June}, school = {University of W{\"u}rzburg}, title = {DIMAQS - Dynamic Identification of Malicious Query Sequences}, type = {{Master Thesis}}, year = 2018 }

  %0 Thesis %1 Jobst2018 %A Jobst, Michael %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T DIMAQS - Dynamic Identification of Malicious Query Sequences
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  {Design of an Industry 4.0 Demonstrator} K{\"o}nig, Philipp; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, September).
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  @mastersthesis{Koenig2018, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {K{\"o}nig, Philipp}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {{Design of an Industry 4.0 Demonstrator}}, type = {{Bachelor Thesis}}, year = 2018 }

  %0 Thesis %1 Koenig2018 %A K{\"o}nig, Philipp %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T {Design of an Industry 4.0 Demonstrator}
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  {Automatic Identification of Parametric Dependencies for Performance Models} Elflein, Sven; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, September).

  W{\"u}rzburg Software Engineering Award sponsored by Bosch Rexroth
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  @mastersthesis{Elflein2018, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Elflein, Sven}, keywords = {t_studentthesis}, month = {September}, note = {<b>W{\"u}rzburg Software Engineering Award sponsored by Bosch Rexroth</b>}, school = {University of W{\"u}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, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T {Automatic Identification of Parametric Dependencies for Performance Models}
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  Fully Automated Deployment and Benchmarking of Security Information and Event Management Systems Heilmann, Jan-Philipp; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, June).
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  @mastersthesis{heilmann2018ba, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Heilmann, Jan-Philipp}, keywords = {t_studentthesis}, month = {June}, school = {University of W{\"u}rzburg}, title = {Fully Automated Deployment and Benchmarking of Security Information and Event Management Systems}, type = {Bachelor Thesis}, year = 2018 }

  %0 Thesis %1 heilmann2018ba %A Heilmann, Jan-Philipp %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T Fully Automated Deployment and Benchmarking of Security Information and Event Management Systems
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  P-Seminar: Umgang mit Mikroelektronik am Beispiel eines Quadrocopters Otto, Rebecca; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, December).
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  @mastersthesis{Otto2018, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Otto, Rebecca}, keywords = {t_studentthesis}, month = {December}, school = {University of W{\"u}rzburg}, title = {P-Seminar: Umgang mit Mikroelektronik am Beispiel eines Quadrocopters}, type = {{Teacher Thesis}}, year = 2018 }

  %0 Thesis %1 Otto2018 %A Otto, Rebecca %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T P-Seminar: Umgang mit Mikroelektronik am Beispiel eines Quadrocopters
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  {Blackbox Learning of Parametric Dependencies for Performance Models from Monitoring Data} Ackermann, Vanessa; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, March).
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  @mastersthesis{Ackermann2018, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Ackermann, Vanessa}, keywords = {t_studentthesis}, month = {March}, school = {University of W{\"u}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, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T {Blackbox Learning of Parametric Dependencies for Performance Models from Monitoring Data}
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  Performance and Security Influence of Augmenting DDoS Protection Systems using SDN and NFV Beierlieb, Lukas; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, January).

  Würzburg Software Engineering Award sponsored by Infosim GmbH & Co. KG
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  @mastersthesis{beierlieb2018ba, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Beierlieb, Lukas}, keywords = {t_studentthesis}, month = {January}, note = {Würzburg Software Engineering Award sponsored by Infosim GmbH & Co. KG}, school = {University of W{\"u}rzburg}, title = {Performance and Security Influence of Augmenting DDoS Protection Systems using SDN and NFV}, type = {Bachelor Thesis}, year = 2018 }

  %0 Thesis %1 beierlieb2018ba %A Beierlieb, Lukas %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T Performance and Security Influence of Augmenting DDoS Protection Systems using SDN and NFV
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  An Evaluation and Benchmarking Solution for Security Information and Event Management Systemes Ben Yahya, Ala Eddine; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2018, March).
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  @mastersthesis{benyahya2018ba, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Ben Yahya, Ala Eddine}, keywords = {t_studentthesis}, month = {March}, school = {University of W{\"u}rzburg}, title = {An Evaluation and Benchmarking Solution for Security Information and Event Management Systemes}, type = {Bachelor Thesis}, year = 2018 }

  %0 Thesis %1 benyahya2018ba %A Ben Yahya, Ala Eddine %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2018 %T An Evaluation and Benchmarking Solution for Security Information and Event Management Systemes

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  {Dynamic Hybrid Forecasting for Self-Aware Systems} Z{\"u}fle, Marwin; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, October).

  W{\"u}rzburg Software Engineering Award sponsored by infosim
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  @mastersthesis{Zuefle2017Dynamic, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Z{\"u}fle, Marwin}, keywords = {t_studentthesis}, month = {October}, note = {<b>W{\"u}rzburg Software Engineering Award sponsored by infosim</b>}, school = {University of W{\"u}rzburg}, title = {{Dynamic Hybrid Forecasting for Self-Aware Systems}}, type = {{Master Thesis}}, year = 2017 }

  %0 Thesis %1 Zuefle2017Dynamic %A Z{\"u}fle, Marwin %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T {Dynamic Hybrid Forecasting for Self-Aware Systems}
• 
  {Model-Based Network Analysis and Optimization} Herrnleben, Stefan; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017).

  Best Master Thesis Award 2018 of GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB)
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  @mastersthesis{herrnleben17, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Herrnleben, Stefan}, keywords = {t_studentthesis}, month = {Oktober}, note = {<b>Best Master Thesis Award 2018 of GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB)</b>}, school = {University of W{\"u}rzburg}, title = {{Model-Based Network Analysis and Optimization}}, type = {{Master Thesis}}, year = 2017 }

  %0 Thesis %1 herrnleben17 %A Herrnleben, Stefan %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T {Model-Based Network Analysis and Optimization}
• 
  {Self-Aware Multidimensional Auto-Scaling} Lesch, Veronika; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, September).

  W{\"u}rzburg Software Engineering Award sponsored by Bosch Rexroth
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  @mastersthesis{Lesch2017Self-Aware, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Lesch, Veronika}, keywords = {t_studentthesis}, month = {September}, note = {<b>W{\"u}rzburg Software Engineering Award sponsored by Bosch Rexroth</b>}, school = {University of W{\"u}rzburg}, title = {{Self-Aware Multidimensional Auto-Scaling}}, type = {{Master Thesis}}, year = 2017 }

  %0 Thesis %1 Lesch2017Self-Aware %A Lesch, Veronika %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T {Self-Aware Multidimensional Auto-Scaling}
• 
  Evaluation der Performanz von IDSes in virtualisierten Netzwerk-Infrastrukturen Stoll, Jonathan; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, June).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{stoll2017MA, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Stoll, Jonathan}, keywords = {t_studentthesis}, month = {June}, school = {University of W{\"u}rzburg}, title = {Evaluation der Performanz von IDSes in virtualisierten Netzwerk-Infrastrukturen}, type = {{Master Thesis}}, year = 2017 }

  %0 Thesis %1 stoll2017MA %A Stoll, Jonathan %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T Evaluation der Performanz von IDSes in virtualisierten Netzwerk-Infrastrukturen
• 
  Cybersecurity Risk Exposure: Towards a Novel Determination Approach based on OSINT Leonhardt, Alexander; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, October).
  • [ BibTeX ]
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  @mastersthesis{Leonhardt2017MA, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Leonhardt, Alexander}, keywords = {t_studentthesis}, month = {October}, school = {University of W{\"u}rzburg}, title = {Cybersecurity Risk Exposure: Towards a Novel Determination Approach based on OSINT}, type = {{Master Thesis}}, year = 2017 }

  %0 Thesis %1 Leonhardt2017MA %A Leonhardt, Alexander %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T Cybersecurity Risk Exposure: Towards a Novel Determination Approach based on OSINT
• 
  {Evaluating Approaches for Online Resource Demand Estimation} Krauss, Torsten; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, September).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{krauss17, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Krauss, Torsten}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {{Evaluating Approaches for Online Resource Demand Estimation}}, type = {{Master Thesis}}, year = 2017 }

  %0 Thesis %1 krauss17 %A Krauss, Torsten %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T {Evaluating Approaches for Online Resource Demand Estimation}
• 
  A Packet-Filtering Firewall for Cloud Computing Hempfling, Christina; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, September).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{Hempfling2017Praktikum, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hempfling, Christina}, keywords = {t_studentthesis}, month = {September}, school = {University of W{\"u}rzburg}, title = {A Packet-Filtering Firewall for Cloud Computing}, type = {Project Thesis}, year = 2017 }

  %0 Thesis %1 Hempfling2017Praktikum %A Hempfling, Christina %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T A Packet-Filtering Firewall for Cloud Computing
• 
  A Framework for a Dynamic Function-Chaining-Controller for Virtualized Network Security Appliances Ciner, Hayreddin; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, October).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{Ciner2017BA, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Ciner, Hayreddin}, keywords = {t_studentthesis}, month = {October}, school = {University of W{\"u}rzburg}, title = {A Framework for a Dynamic Function-Chaining-Controller for Virtualized Network Security Appliances}, type = {{Bachelor Thesis}}, year = 2017 }

  %0 Thesis %1 Ciner2017BA %A Ciner, Hayreddin %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T A Framework for a Dynamic Function-Chaining-Controller for Virtualized Network Security Appliances
• 
  {Cluster Analysis for Resource Demand Estimation} Schaefers, Karsten; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2017, August).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{Schaefers2017, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Schaefers, Karsten}, keywords = {t_studentthesis}, month = {August}, school = {University of W{\"u}rzburg}, title = {{Cluster Analysis for Resource Demand Estimation}}, type = {{BachelorThesis}}, year = 2017 }

  %0 Thesis %1 Schaefers2017 %A Schaefers, Karsten %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2017 %T {Cluster Analysis for Resource Demand Estimation}

• 
  P-Seminar: Raspberry Pi als Mediencenter für die Oberstufe Frauendorfer, Katja; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2016, April).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{Frauendorfer2016, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Frauendorfer, Katja}, keywords = {t_studentthesis}, month = {April}, school = {University of W{\"u}rzburg}, title = {P-Seminar: Raspberry Pi als Mediencenter für die Oberstufe}, type = {{Teacher Thesis}}, year = 2016 }

  %0 Thesis %1 Frauendorfer2016 %A Frauendorfer, Katja %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2016 %T P-Seminar: Raspberry Pi als Mediencenter für die Oberstufe
• 
  {Reliable Resource Demand Estimation} Grohmann, Johannes; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2016, October).
  • [ Abstract ]
  • [ BibTeX ]
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  Resource demands are key parameters of performance models used to predict the behavior of data centers. They define the amount of time a request spends obtaining a limited resource like the CPU. Requests can be grouped into different workload classes. Measuring these resource demands is usually unfeasible in practice. Therefore, several different approaches to estimate the resource demands of different workload classes exist. However, different use-cases with individual properties influence the accuracy of the estimators. Among others the number of different workload classes to estimate is known to have an impact on the solution quality, but affects some approaches more than others. Additionally, most approaches offer specific parameters to configure and optimize the estimators. Nevertheless, in order to optimize the parameters of one estimation approach or to choose the best estimator for a given scenario either expert knowledge or exhaustive testing is required. While some works on comparing different approaches and configurations exist, we extend this by learning on a given training set and specially adapting the estimation approaches in order to optimize performance for the required target scenario. We simplify automated resource demand estimation by designing a framework for ready-to-use reliable resource demand estimation. In order to do so, we develop generic algorithms that can be used to autonomously optimize parameter configurations of black-box estimation approaches on a given training set. Secondly, machine learning algorithms analyze the behavior of the resource demand estimators on different training traces and automatically pick the best approach for a prior unseen trace. The framework is modularized and configurable and can be trained on any kind of trace data. We implement different algorithms for optimization as well as machine learning and evaluate them on a training set containing measurements of a real system. The results show that parameter optimization is very promising and can increase the accuracy of single approaches of up to 10%. When recommending one approach as opposed to running all simultaneously, comparable results can be achieved, while saving more than 50% of the runtime. However, a combination of both approaches does not seem useful on our data set.

  @mastersthesis{grohmann16, abstract = {Resource demands are key parameters of performance models used to predict the behavior of data centers. They define the amount of time a request spends obtaining a limited resource like the CPU. Requests can be grouped into different workload classes. Measuring these resource demands is usually unfeasible in practice. Therefore, several different approaches to estimate the resource demands of different workload classes exist. However, different use-cases with individual properties influence the accuracy of the estimators. Among others the number of different workload classes to estimate is known to have an impact on the solution quality, but affects some approaches more than others. Additionally, most approaches offer specific parameters to configure and optimize the estimators. Nevertheless, in order to optimize the parameters of one estimation approach or to choose the best estimator for a given scenario either expert knowledge or exhaustive testing is required. While some works on comparing different approaches and configurations exist, we extend this by learning on a given training set and specially adapting the estimation approaches in order to optimize performance for the required target scenario. We simplify automated resource demand estimation by designing a framework for ready-to-use reliable resource demand estimation. In order to do so, we develop generic algorithms that can be used to autonomously optimize parameter configurations of black-box estimation approaches on a given training set. Secondly, machine learning algorithms analyze the behavior of the resource demand estimators on different training traces and automatically pick the best approach for a prior unseen trace. The framework is modularized and configurable and can be trained on any kind of trace data. We implement different algorithms for optimization as well as machine learning and evaluate them on a training set containing measurements of a real system. The results show that parameter optimization is very promising and can increase the accuracy of single approaches of up to 10%. When recommending one approach as opposed to running all simultaneously, comparable results can be achieved, while saving more than 50% of the runtime. However, a combination of both approaches does not seem useful on our data set.}, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Grohmann, Johannes}, keywords = {t_studentthesis}, month = {October}, school = {University of W{\"u}rzburg}, title = {{Reliable Resource Demand Estimation}}, type = {{Master Thesis}}, year = 2016 }

  %0 Thesis %1 grohmann16 %A Grohmann, Johannes %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2016 %T {Reliable Resource Demand Estimation} %X Resource demands are key parameters of performance models used to predict the behavior of data centers. They define the amount of time a request spends obtaining a limited resource like the CPU. Requests can be grouped into different workload classes. Measuring these resource demands is usually unfeasible in practice. Therefore, several different approaches to estimate the resource demands of different workload classes exist. However, different use-cases with individual properties influence the accuracy of the estimators. Among others the number of different workload classes to estimate is known to have an impact on the solution quality, but affects some approaches more than others. Additionally, most approaches offer specific parameters to configure and optimize the estimators. Nevertheless, in order to optimize the parameters of one estimation approach or to choose the best estimator for a given scenario either expert knowledge or exhaustive testing is required. While some works on comparing different approaches and configurations exist, we extend this by learning on a given training set and specially adapting the estimation approaches in order to optimize performance for the required target scenario. We simplify automated resource demand estimation by designing a framework for ready-to-use reliable resource demand estimation. In order to do so, we develop generic algorithms that can be used to autonomously optimize parameter configurations of black-box estimation approaches on a given training set. Secondly, machine learning algorithms analyze the behavior of the resource demand estimators on different training traces and automatically pick the best approach for a prior unseen trace. The framework is modularized and configurable and can be trained on any kind of trace data. We implement different algorithms for optimization as well as machine learning and evaluate them on a training set containing measurements of a real system. The results show that parameter optimization is very promising and can increase the accuracy of single approaches of up to 10%. When recommending one approach as opposed to running all simultaneously, comparable results can be achieved, while saving more than 50% of the runtime. However, a combination of both approaches does not seem useful on our data set.
• 
  Assessment of the Influence of I/O-Load Factors on Service Migration Performance Liu, Xiaofen; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2016, December).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{Liu2016, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Liu, Xiaofen}, keywords = {t_studentthesis}, month = {December}, school = {University of W{\"u}rzburg}, title = {Assessment of the Influence of I/O-Load Factors on Service Migration Performance}, type = {{Master Thesis}}, year = 2016 }

  %0 Thesis %1 Liu2016 %A Liu, Xiaofen %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2016 %T Assessment of the Influence of I/O-Load Factors on Service Migration Performance
• 
  Cyber-Physical Systems - Models and Security Aspects Hempfling, Christina; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2016, December).
  • [ BibTeX ]
  • [ EndNote ]
  @mastersthesis{Hempfling2016, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Hempfling, Christina}, keywords = {t_studentthesis}, month = {December}, school = {University of W{\"u}rzburg}, title = {Cyber-Physical Systems - Models and Security Aspects}, type = {{Master Thesis}}, year = 2016 }

  %0 Thesis %1 Hempfling2016 %A Hempfling, Christina %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2016 %T Cyber-Physical Systems - Models and Security Aspects
• 
  {Local Approximation of NFV Workloads} Schmitt, Norbert; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2016, October).
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  @mastersthesis{Schmitt2015, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Schmitt, Norbert}, keywords = {t_studentthesis}, month = {October}, school = {University of W{\"u}rzburg}, title = {{Local Approximation of NFV Workloads}}, type = {{Master Thesis}}, year = 2016 }

  %0 Thesis %1 Schmitt2015 %A Schmitt, Norbert %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2016 %T {Local Approximation of NFV Workloads}
• 
  {Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism} Bauer, Andr{\’e}; Thesis; University of W{\"u}rzburg; Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany. (2016).
  • [ Abstract ]
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  In order to guarantee a reliable service quality to their customers and due to the increasing complexity of cloud applications, SaaS cloud providers usually run their business critical applications with a fixed amount of resources. This approach has drawbacks such as a higher costs (e.g., bad energy efficiency, if the system is not fully utilized), or bad performance regarding unexpected work peaks. Amazon with its EC2, for instance, provides a configurable scheduled-based and rule-based auto-scaler. However, this reactive scaling has a latency depending on resource type in the order of minutes. Therefore, proactive and application aware auto-scalers are required - intelligent controllers, which can reconfigure the system on time, to ensure high availability and constant performance under changing conditions. The existing auto-scalers can be classified into five groups and prominent examples of each class were investigated. The result of this observation is that the existing controllers are either application-specific or generic. I.e. the auto-scalers can either perform well only on the chosen system or they perform far beyond their means. In this thesis, a novel proactive, application-aware elasticity mechanism called Chameleon is introduced. The proposed controller employs established forecast methods for short-, mid- and long-term predictions of the arriving load intensity, application knowledge, and resource demand estimation to calculate the required resources per work unit. While taking these information into account, the mechanism reconfigures the deployment of an application in a way that the supply of resources matches the current and estimated future demand for resources. I.e., Chameleon consists of two mechanisms: (I) a reactive rule-based controller as fall-back insurance and (II) a proactive controller which has three major building blocks: (a) continuous workload forecasting (using a modified version of WCF) with dynamic forecast method selection, (b) a descriptive performance model/ application knowledge (DML Model@RunTime), and (c) optimized resource demand estimation approaches (using the tool LibReDE). In the evaluation, Chameleon is compared against four different state-of-the-art controllers in a private CloudStack-based environment. The BUNGEE elasticity benchmark framework and metrics are used to conduct and analyse the row of experiments. As workload scenarios, a CPU intensive http application that solves nxn matrices (whereas n is the request parameter) and SPECjEnterprise2010 is used. The applications are driven with variable load profiles (extracted with LIMBO from the FIFA World Cup 1998 trace) generated with JMeter (instead of Faban - as classically used by SPECjEnterprise2010). During the CPU intensive scenario Chameleon achieves the best scaling behaviour (based on user and elasticity metrics) and in the SPECjEnterprise2010 the second best user metrics.

  @mastersthesis{Bauer2016, abstract = {In order to guarantee a reliable service quality to their customers and due to the increasing complexity of cloud applications, SaaS cloud providers usually run their business critical applications with a fixed amount of resources. This approach has drawbacks such as a higher costs (e.g., bad energy efficiency, if the system is not fully utilized), or bad performance regarding unexpected work peaks. Amazon with its EC2, for instance, provides a configurable scheduled-based and rule-based auto-scaler. However, this reactive scaling has a latency depending on resource type in the order of minutes. Therefore, proactive and application aware auto-scalers are required - intelligent controllers, which can reconfigure the system on time, to ensure high availability and constant performance under changing conditions. The existing auto-scalers can be classified into five groups and prominent examples of each class were investigated. The result of this observation is that the existing controllers are either application-specific or generic. I.e. the auto-scalers can either perform well only on the chosen system or they perform far beyond their means. In this thesis, a novel proactive, application-aware elasticity mechanism called Chameleon is introduced. The proposed controller employs established forecast methods for short-, mid- and long-term predictions of the arriving load intensity, application knowledge, and resource demand estimation to calculate the required resources per work unit. While taking these information into account, the mechanism reconfigures the deployment of an application in a way that the supply of resources matches the current and estimated future demand for resources. I.e., Chameleon consists of two mechanisms: (I) a reactive rule-based controller as fall-back insurance and (II) a proactive controller which has three major building blocks: (a) continuous workload forecasting (using a modified version of WCF) with dynamic forecast method selection, (b) a descriptive performance model/ application knowledge (DML Model@RunTime), and (c) optimized resource demand estimation approaches (using the tool LibReDE). In the evaluation, Chameleon is compared against four different state-of-the-art controllers in a private CloudStack-based environment. The BUNGEE elasticity benchmark framework and metrics are used to conduct and analyse the row of experiments. As workload scenarios, a CPU intensive http application that solves nxn matrices (whereas n is the request parameter) and SPECjEnterprise2010 is used. The applications are driven with variable load profiles (extracted with LIMBO from the FIFA World Cup 1998 trace) generated with JMeter (instead of Faban - as classically used by SPECjEnterprise2010). During the CPU intensive scenario Chameleon achieves the best scaling behaviour (based on user and elasticity metrics) and in the SPECjEnterprise2010 the second best user metrics.}, address = {Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany}, author = {Bauer, Andr{\'e}}, keywords = {t_studentthesis}, month = {Septembter}, school = {University of W{\"u}rzburg}, title = {{Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism}}, type = {{Master Thesis}}, year = 2016 }

  %0 Thesis %1 Bauer2016 %A Bauer, Andr{\'e} %C Am Hubland, Informatikgeb{\"a}ude, 97074 W{\"u}rzburg, Germany %D 2016 %T {Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism} %X In order to guarantee a reliable service quality to their customers and due to the increasing complexity of cloud applications, SaaS cloud providers usually run their business critical applications with a fixed amount of resources. This approach has drawbacks such as a higher costs (e.g., bad energy efficiency, if the system is not fully utilized), or bad performance regarding unexpected work peaks. Amazon with its EC2, for instance, provides a configurable scheduled-based and rule-based auto-scaler. However, this reactive scaling has a latency depending on resource type in the order of minutes. Therefore, proactive and application aware auto-scalers are required - intelligent controllers, which can reconfigure the system on time, to ensure high availability and constant performance under changing conditions. The existing auto-scalers can be classified into five groups and prominent examples of each class were investigated. The result of this observation is that the existing controllers are either application-specific or generic. I.e. the auto-scalers can either perform well only on the chosen system or they perform far beyond their means. In this thesis, a novel proactive, application-aware elasticity mechanism called Chameleon is introduced. The proposed controller employs established forecast methods for short-, mid- and long-term predictions of the arriving load intensity, application knowledge, and resource demand estimation to calculate the required resources per work unit. While taking these information into account, the mechanism reconfigures the deployment of an application in a way that the supply of resources matches the current and estimated future demand for resources. I.e., Chameleon consists of two mechanisms: (I) a reactive rule-based controller as fall-back insurance and (II) a proactive controller which has three major building blocks: (a) continuous workload forecasting (using a modified version of WCF) with dynamic forecast method selection, (b) a descriptive performance model/ application knowledge (DML Model@RunTime), and (c) optimized resource demand estimation approaches (using the tool LibReDE). In the evaluation, Chameleon is compared against four different state-of-the-art controllers in a private CloudStack-based environment. The BUNGEE elasticity benchmark framework and metrics are used to conduct and analyse the row of experiments. As workload scenarios, a CPU intensive http application that solves nxn matrices (whereas n is the request parameter) and SPECjEnterprise2010 is used. The applications are driven with variable load profiles (extracted with LIMBO from the FIFA World Cup 1998 trace) generated with JMeter (instead of Faban - as classically used by SPECjEnterprise2010). During the CPU intensive scenario Chameleon achieves the best scaling behaviour (based on user and elasticity metrics) and in the SPECjEnterprise2010 the second best user metrics.

• 
  {Modeling Variations in Load Intensity Profiles} von Kistowski, J{\’o}akim; Thesis; {Karlsruhe Institute of Technology (KIT)}; Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2014, March).

  Gesellschaft zur F{\"o}rderung des Forschungstransfers (GFFT) Prize "Best Master Thesis"
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  {Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, I identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The high-level Descartes Load Intensity Meta-Model (hl-DLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts, and noise parts. Using these parameters, hl-DLIM is capable of describing a subset of most common load intensity variations. During the work on this thesis I developed LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on DLIM and hl-DLIM as underlying modeling formalisms. LIMBO provides visualization for DLIM instances and a model creation wizard based on hl-DLIM parameters. It also offers three automated model extraction processes with which to extract DLIM and hl-DLIM instances from existing arrival rate traces. It also offers a model-to-model transformation from hl-DLIM to DLIM. I demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy, having an average median deviation of 19.9\% from the original trace. This is done by comparing nine different real life traces, which were measured over duration of two weeks to seven months, to their respective model instances as extracted by the automated model extraction processes. I also evaluate the usability and accessibility of LIMBO based on a questionnaire which was answered by eight computer scientists from five different organizations within the performance engineering community.}

  @mastersthesis{Kistowski2014, abstract = {{Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, I identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The high-level Descartes Load Intensity Meta-Model (hl-DLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts, and noise parts. Using these parameters, hl-DLIM is capable of describing a subset of most common load intensity variations. During the work on this thesis I developed LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on DLIM and hl-DLIM as underlying modeling formalisms. LIMBO provides visualization for DLIM instances and a model creation wizard based on hl-DLIM parameters. It also offers three automated model extraction processes with which to extract DLIM and hl-DLIM instances from existing arrival rate traces. It also offers a model-to-model transformation from hl-DLIM to DLIM. I demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy, having an average median deviation of 19.9\% from the original trace. This is done by comparing nine different real life traces, which were measured over duration of two weeks to seven months, to their respective model instances as extracted by the automated model extraction processes. I also evaluate the usability and accessibility of LIMBO based on a questionnaire which was answered by eight computer scientists from five different organizations within the performance engineering community.}}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {von Kistowski, J{\'o}akim}, keywords = {t_studentthesis}, month = {March}, note = {<b>Gesellschaft zur F{\"o}rderung des Forschungstransfers (GFFT) Prize "Best Master Thesis"</b>}, school = {{Karlsruhe Institute of Technology (KIT)}}, title = {{Modeling Variations in Load Intensity Profiles}}, type = {{Master Thesis}}, year = 2014 }

  %0 Thesis %1 Kistowski2014 %A von Kistowski, J{\'o}akim %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2014 %T {Modeling Variations in Load Intensity Profiles} %X {Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, I identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The high-level Descartes Load Intensity Meta-Model (hl-DLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts, and noise parts. Using these parameters, hl-DLIM is capable of describing a subset of most common load intensity variations. During the work on this thesis I developed LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on DLIM and hl-DLIM as underlying modeling formalisms. LIMBO provides visualization for DLIM instances and a model creation wizard based on hl-DLIM parameters. It also offers three automated model extraction processes with which to extract DLIM and hl-DLIM instances from existing arrival rate traces. It also offers a model-to-model transformation from hl-DLIM to DLIM. I demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy, having an average median deviation of 19.9\% from the original trace. This is done by comparing nine different real life traces, which were measured over duration of two weeks to seven months, to their respective model instances as extracted by the automated model extraction processes. I also evaluate the usability and accessibility of LIMBO based on a questionnaire which was answered by eight computer scientists from five different organizations within the performance engineering community.}

• 
  {Workload Classification and Forecasting} Herbst, Nikolas Roman; Thesis; {Karlsruhe Institute of Technology (KIT)}; Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2012).

  Forschungszentrum Informatik (FZI) Prize "Best Diploma Thesis"
  • [ Abstract ]
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  {Virtualization technologies enable dynamic allocation of computing resources to execution environments at run-time. To exploit optimisation potential that comes with these degrees of freedom, forecasts of the arriving work's intensity are valuable information, to continuously ensure a defined quality of service (QoS) definition and at the same time to improve the efficiency of the resource utilisation. Time series analysis offers a broad spectrum of methods for calculation of forecasts based on periodically monitored values. Related work in the field of proactive resource provisioning mostly concentrate on single methods of the time series analysis and their individual optimisation potential. This way, usable forecast results are achieved only in certain situations. In this thesis, established methods of the time series analysis are surveyed and grouped concerning their strengths and weaknesses. A dynamic approach is presented that selects based on a decision tree and direct feedback cycles, capturing the forecast accuracy, the suitable method for a given situation. The user needs to provide only his general forecast objectives. An implementation of the introduced theoretical approach is presented that continuously provides forecasts of the arriving work's intensity in configurable intervals and with controllable computational overhead during run-time. Based on real-world intensity traces, a number of different experiments and a case study is conducted. The results show, that by use of the implementation the relative error of the forecast points in relation to the arriving observations is reduced by 63% in average compared to the results of a statically selected, sophisticated method. In a case study, between 52% and 70% of the violations of a given service level agreement are prevented by applying proactive resource provisioning based on the forecast results of the introduced implementation.}

  @mastersthesis{Herbst2012a, abstract = {{Virtualization technologies enable dynamic allocation of computing resources to execution environments at run-time. To exploit optimisation potential that comes with these degrees of freedom, forecasts of the arriving work's intensity are valuable information, to continuously ensure a defined quality of service (QoS) definition and at the same time to improve the efficiency of the resource utilisation. Time series analysis offers a broad spectrum of methods for calculation of forecasts based on periodically monitored values. Related work in the field of proactive resource provisioning mostly concentrate on single methods of the time series analysis and their individual optimisation potential. This way, usable forecast results are achieved only in certain situations. In this thesis, established methods of the time series analysis are surveyed and grouped concerning their strengths and weaknesses. A dynamic approach is presented that selects based on a decision tree and direct feedback cycles, capturing the forecast accuracy, the suitable method for a given situation. The user needs to provide only his general forecast objectives. An implementation of the introduced theoretical approach is presented that continuously provides forecasts of the arriving work's intensity in configurable intervals and with controllable computational overhead during run-time. Based on real-world intensity traces, a number of different experiments and a case study is conducted. The results show, that by use of the implementation the relative error of the forecast points in relation to the arriving observations is reduced by 63% in average compared to the results of a statically selected, sophisticated method. In a case study, between 52% and 70% of the violations of a given service level agreement are prevented by applying proactive resource provisioning based on the forecast results of the introduced implementation.}}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {Herbst, Nikolas Roman}, keywords = {t_studentthesis}, note = {<b>Forschungszentrum Informatik (FZI) Prize "Best Diploma Thesis"</b>}, school = {{Karlsruhe Institute of Technology (KIT)}}, title = {{Workload Classification and Forecasting}}, type = {{Diploma Thesis}}, year = 2012 }

  %0 Thesis %1 Herbst2012a %A Herbst, Nikolas Roman %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2012 %T {Workload Classification and Forecasting} %X {Virtualization technologies enable dynamic allocation of computing resources to execution environments at run-time. To exploit optimisation potential that comes with these degrees of freedom, forecasts of the arriving work's intensity are valuable information, to continuously ensure a defined quality of service (QoS) definition and at the same time to improve the efficiency of the resource utilisation. Time series analysis offers a broad spectrum of methods for calculation of forecasts based on periodically monitored values. Related work in the field of proactive resource provisioning mostly concentrate on single methods of the time series analysis and their individual optimisation potential. This way, usable forecast results are achieved only in certain situations. In this thesis, established methods of the time series analysis are surveyed and grouped concerning their strengths and weaknesses. A dynamic approach is presented that selects based on a decision tree and direct feedback cycles, capturing the forecast accuracy, the suitable method for a given situation. The user needs to provide only his general forecast objectives. An implementation of the introduced theoretical approach is presented that continuously provides forecasts of the arriving work's intensity in configurable intervals and with controllable computational overhead during run-time. Based on real-world intensity traces, a number of different experiments and a case study is conducted. The results show, that by use of the implementation the relative error of the forecast points in relation to the arriving observations is reduced by 63% in average compared to the results of a statically selected, sophisticated method. In a case study, between 52% and 70% of the violations of a given service level agreement are prevented by applying proactive resource provisioning based on the forecast results of the introduced implementation.}

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  {Quantifying the Impact of Configuration Space for Elasticity Benchmarking} Herbst, Nikolas Roman; Thesis; {Karlsruhe Institute of Technology (KIT)}; Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2011).
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  {Elasticity is the ability of a software system to dynamically adapt the amount of the resources it provides to clients as their workloads increase or decrease. In the context of cloud computing, automated resizing of a virtual machine's resources can be considered as a key step towards optimisation of a system's cost and energy efficiency. Existing work on cloud computing is limited to the technical view of implementing elastic systems, and definitions of scalability have not been extended to cover elasticity. This study thesis presents a detailed discussion of elasticity, proposes metrics as well as measurement techniques, and outlines next steps for enabling comparisons between cloud computing offerings on the basis of elasticity. I discuss results of our work on measuring elasticity of thread pools provided by the Java virtual machine, as well as an experiment setup for elastic CPU time slice resizing in a virtualized environment. An experiment setup is presented as future work for dynamically adding and removing z/VM Linux virtual machine instances to a performance relevant group of virtualized servers.}

  @mastersthesis{Herbst2011a, abstract = {{Elasticity is the ability of a software system to dynamically adapt the amount of the resources it provides to clients as their workloads increase or decrease. In the context of cloud computing, automated resizing of a virtual machine's resources can be considered as a key step towards optimisation of a system's cost and energy efficiency. Existing work on cloud computing is limited to the technical view of implementing elastic systems, and definitions of scalability have not been extended to cover elasticity. This study thesis presents a detailed discussion of elasticity, proposes metrics as well as measurement techniques, and outlines next steps for enabling comparisons between cloud computing offerings on the basis of elasticity. I discuss results of our work on measuring elasticity of thread pools provided by the Java virtual machine, as well as an experiment setup for elastic CPU time slice resizing in a virtualized environment. An experiment setup is presented as future work for dynamically adding and removing z/VM Linux virtual machine instances to a performance relevant group of virtualized servers.}}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {Herbst, Nikolas Roman}, keywords = {t_studentthesis}, school = {{Karlsruhe Institute of Technology (KIT)}}, title = {{Quantifying the Impact of Configuration Space for Elasticity Benchmarking}}, type = {{Study Thesis}}, year = 2011 }

  %0 Thesis %1 Herbst2011a %A Herbst, Nikolas Roman %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2011 %T {Quantifying the Impact of Configuration Space for Elasticity Benchmarking} %X {Elasticity is the ability of a software system to dynamically adapt the amount of the resources it provides to clients as their workloads increase or decrease. In the context of cloud computing, automated resizing of a virtual machine's resources can be considered as a key step towards optimisation of a system's cost and energy efficiency. Existing work on cloud computing is limited to the technical view of implementing elastic systems, and definitions of scalability have not been extended to cover elasticity. This study thesis presents a detailed discussion of elasticity, proposes metrics as well as measurement techniques, and outlines next steps for enabling comparisons between cloud computing offerings on the basis of elasticity. I discuss results of our work on measuring elasticity of thread pools provided by the Java virtual machine, as well as an experiment setup for elastic CPU time slice resizing in a virtualized environment. An experiment setup is presented as future work for dynamically adding and removing z/VM Linux virtual machine instances to a performance relevant group of virtualized servers.}
• 
  {Defining and Measuring Workloads for Elasticity Benchmarking} von Kistowski, J{\’o}akim; Thesis; {Karlsruhe Institute of Technology (KIT)}; Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2011).
  • [ BibTeX ]
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  @mastersthesis{Kistowski2011a, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {von Kistowski, J{\'o}akim}, keywords = {t_studentthesis}, school = {{Karlsruhe Institute of Technology (KIT)}}, title = {{Defining and Measuring Workloads for Elasticity Benchmarking}}, type = {{Bachelor Thesis}}, year = 2011 }

  %0 Thesis %1 Kistowski2011a %A von Kistowski, J{\'o}akim %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2011 %T {Defining and Measuring Workloads for Elasticity Benchmarking}

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  Combination of measurement and model based approaches for performance prediction in service oriented systems Krebs, Rouven; Thesis; University of Applied Sciences Karlsruhe; Moltkestr. 30, 76133 Karlsruhe, Germany. (2010, October).
  • [ BibTeX ]
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  @mastersthesis{Krebs2010a, address = {Moltkestr. 30, 76133 Karlsruhe, Germany}, author = {Krebs, Rouven}, keywords = {t_studentthesis}, month = {October}, school = {University of Applied Sciences Karlsruhe}, title = {Combination of measurement and model based approaches for performance prediction in service oriented systems}, year = 2010 }

  %0 Thesis %1 Krebs2010a %A Krebs, Rouven %C Moltkestr. 30, 76133 Karlsruhe, Germany %D 2010 %T Combination of measurement and model based approaches for performance prediction in service oriented systems
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  Focusing the Optimization of Software Architecture Models Using Non-Functional Requirements Noorshams, Qais; Thesis; Karlsruhe Institute of Technology; Karlsruhe, Germany. (2010).
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  @mastersthesis{No2010-Masterthesis-Optimization, address = {Karlsruhe, Germany}, author = {Noorshams, Qais}, keywords = {t_studentthesis}, school = {Karlsruhe Institute of Technology}, title = {Focusing the Optimization of Software Architecture Models Using Non-Functional Requirements}, year = 2010 }

  %0 Thesis %1 No2010-Masterthesis-Optimization %A Noorshams, Qais %C Karlsruhe, Germany %D 2010 %T Focusing the Optimization of Software Architecture Models Using Non-Functional Requirements

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  {Performance Modeling of Storage Virtualization} Huber, Nikolaus; Thesis; Universit{{\"a}}t Karlsruhe (TH); Karlsruhe, Germany. (2009, April).

  GFFT Prize
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  @mastersthesis{Hu2009-UKA-PerfMod, address = {Karlsruhe, Germany}, author = {Huber, Nikolaus}, keywords = {t_studentthesis}, month = {April}, note = {<b>GFFT Prize</b>}, school = {Universit{{\"a}}t Karlsruhe (TH)}, title = {{Performance Modeling of Storage Virtualization}}, year = 2009 }

  %0 Thesis %1 Hu2009-UKA-PerfMod %A Huber, Nikolaus %C Karlsruhe, Germany %D 2009 %T {Performance Modeling of Storage Virtualization}
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  {Automated Extraction of Palladio Component Models from Running Enterprise Java Applications} Brosig, Fabian; Thesis; Universit{{\"a}}t Karlsruhe (TH); Karlsruhe, Germany. (2009, June).

  FZI Prize "Best Diploma Thesis"
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  @mastersthesis{brosig2009a, address = {Karlsruhe, Germany}, author = {Brosig, Fabian}, keywords = {t_studentthesis}, month = {June}, note = {<b>FZI Prize "Best Diploma Thesis"</b>}, school = {Universit{{\"a}}t Karlsruhe (TH)}, title = {{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}}, year = 2009 }

  %0 Thesis %1 brosig2009a %A Brosig, Fabian %C Karlsruhe, Germany %D 2009 %T {Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}
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  {Software implementation of the simulation environment of the system for multistage parallel processing of teletraffic in the node of the packet-switched network for traffic control algorithms performance analysis purposes.} Rygielski, Piotr; Thesis; Wroclaw University of Technology; Wybrze{\.z}e Wyspia{\’n}skiego 27, 50-370 Wroclaw, Poland. (2009, July).

  The work is written in Polish language.
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  In this thesis, the problem of controlling network traffic for guaranteeing demanded Quality of Service level was presented. An architecture for network node with parallel processing units was considered and exemplary problems of teletraffic control were formulated. The process of service is a multistage process. A simulation environment was designed and developed for the needs of quantitatively analysis of control algorithms placed on the node. There were load balancing and packet scheduling algorithms implemented. Exemplary simulations were run to present functionality of environment and the way, users should implement their own, new algorithms was presented. At the end, the work was summarised and the future work plans were presented.

  @mastersthesis{rygielski2009a, abstract = {In this thesis, the problem of controlling network traffic for guaranteeing demanded Quality of Service level was presented. An architecture for network node with parallel processing units was considered and exemplary problems of teletraffic control were formulated. The process of service is a multistage process. A simulation environment was designed and developed for the needs of quantitatively analysis of control algorithms placed on the node. There were load balancing and packet scheduling algorithms implemented. Exemplary simulations were run to present functionality of environment and the way, users should implement their own, new algorithms was presented. At the end, the work was summarised and the future work plans were presented.}, address = {Wybrze{\.z}e Wyspia{\'n}skiego 27, 50-370 Wroclaw, Poland}, author = {Rygielski, Piotr}, keywords = {t_studentthesis}, month = {July}, note = {The work is written in Polish language.}, school = {Wroclaw University of Technology}, title = {{Software implementation of the simulation environment of the system for multistage parallel processing of teletraffic in the node of the packet-switched network for traffic control algorithms performance analysis purposes.}}, year = 2009 }

  %0 Thesis %1 rygielski2009a %A Rygielski, Piotr %C Wybrze{\.z}e Wyspia{\'n}skiego 27, 50-370 Wroclaw, Poland %D 2009 %T {Software implementation of the simulation environment of the system for multistage parallel processing of teletraffic in the node of the packet-switched network for traffic control algorithms performance analysis purposes.} %X In this thesis, the problem of controlling network traffic for guaranteeing demanded Quality of Service level was presented. An architecture for network node with parallel processing units was considered and exemplary problems of teletraffic control were formulated. The process of service is a multistage process. A simulation environment was designed and developed for the needs of quantitatively analysis of control algorithms placed on the node. There were load balancing and packet scheduling algorithms implemented. Exemplary simulations were run to present functionality of environment and the way, users should implement their own, new algorithms was presented. At the end, the work was summarised and the future work plans were presented.

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  {{E}ine {M}anagementinfrastruktur f{\"u}r die {\"U}berwachung komponierter {W}ebservices} Rathfelder, Christoph; Thesis; Universit{"a}t Karlsruhe (TH); Karlsruhe. (2007).
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  @mastersthesis{rathfelder2007b, address = {Karlsruhe}, author = {Rathfelder, Christoph}, keywords = {t_studentthesis}, school = {Universit{"a}t Karlsruhe (TH)}, title = {{{E}ine {M}anagementinfrastruktur f{\"u}r die {\"U}berwachung komponierter {W}ebservices}}, year = 2007 }

  %0 Thesis %1 rathfelder2007b %A Rathfelder, Christoph %C Karlsruhe %D 2007 %T {{E}ine {M}anagementinfrastruktur f{\"u}r die {\"U}berwachung komponierter {W}ebservices}

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  {Design and Development of an Electronic Commerce Environment} Kounev, Samuel; Thesis; University of Sofia; Sofia, Bulgaria. (1999, August).
  • [ BibTeX ]
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  @mastersthesis{Ko1999-UNI_SOFIA-MSc_Thesis, address = {Sofia, Bulgaria}, author = {Kounev, Samuel}, keywords = {t_studentthesis}, month = {August}, school = {University of Sofia}, title = {{Design and Development of an Electronic Commerce Environment}}, year = 1999 }

  %0 Thesis %1 Ko1999-UNI_SOFIA-MSc_Thesis %A Kounev, Samuel %C Sofia, Bulgaria %D 1999 %T {Design and Development of an Electronic Commerce Environment}