Supervised student theses

[ 2020 ] [ 2019 ] [ 2018 ] [ 2017 ] [ 2016 ] [ 2015 ] [ 2014 ] [ 2013 ] [ 2012 ] [ 2011 ] [ 2010 ] [ 2009 ]

2020 [ nach oben ]

Combining Platooning with Corridor Control Systems. Johnson, Andre; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, November).

[ BibTeX ] [ EndNote ]

Situation-Aware Meta-Optimization of Platooning Coordination Strategies. Hefter, Johannes; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, September).

[ BibTeX ] [ EndNote ]

Optimal Platooning Coordination using Multiple Objectives. Hoang Tran, Ha Giang; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, September).

[ BibTeX ] [ EndNote ]

Predicting Failures by Means of Machine Learning Methods on the Example of an Industrial Press. Agne, Joachim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2020, February).

[ BibTeX ] [ EndNote ]

2019 [ nach oben ]

Comparison of Compensation Mechanisms for Intra-Platoon Position Effects. Stubenrauch, Kevin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, July).

Würzburg Software Engineering Award sponsored by Bosch Rexroth

[ BibTeX ] [ EndNote ]

Towards Self-Aware Multirotor Formations. Kaiser, Dennis; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, September).

Würzburg Software Engineering Award sponsored by infosim

[ BibTeX ] [ EndNote ] [ Download ]

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

[ BibTeX ] [ EndNote ] [ Download ]

Erweiterung von Komponenten-basierten Performance Modellen für Micro-service Architekturen. Hick, Lars; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, February).

[ BibTeX ] [ EndNote ]

An Evaluation Tool for Cost-Aware Vehicle Route Calculation. Hadry, Marius; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, August).

[ BibTeX ] [ EndNote ]

Neukonzeptualisierung des Programmiervorkurses mit Fokus auf Übung und Vertiefung. Migendt, Christian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, July).

[ BibTeX ] [ EndNote ]

Heat-aware and CPU Boost-oriented Server Load Rotation. Knapp, Andreas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, November).

[ BibTeX ] [ EndNote ]

Analysis of Centralized Platooning Coordination Approaches. Dalhof, Florian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, August).

[ BibTeX ] [ EndNote ]

An Approach for Domain-specific Data Extraction and Feature Engineering for Sports Data Analytics. Dreher, Tim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, October).

[ BibTeX ] [ EndNote ]

Designing a Generic Workflow for Frequency-based Feature Engineering of Sports Data Using Symbolic Fourier Approximation. Erhard, Florian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, September).

[ BibTeX ] [ EndNote ]

Implementation and Evaluation of a Group Encryption Scheme. Ten, Peter; Thesis; University of Würzburg. (2019, December).

[ BibTeX ] [ EndNote ]

Mosquitto Swatter: Demonstrating MQTT Vulnerabilities. Metzler, Samuel; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2019, February).

[ BibTeX ] [ EndNote ]

2018 [ nach oben ]

Automatisiertes Forecasting mit Feature Engineering. Augustin, Britta; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, April).

[ BibTeX ] [ EndNote ]

Fully Automated Deployment and Benchmarking of Security Information and Event Management Systems. Heilmann, Jan-Philipp; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, June).

[ BibTeX ] [ EndNote ]

Performance Evaluation of Security Network Function Reordering. Fella, Nicolas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, December).

[ BibTeX ] [ EndNote ]

Time Series Forecasting: A Recommendation for Method Selection. Ort, Jannik; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).

[ BibTeX ] [ EndNote ]

Design of an Industry 4.0 Demonstrator. König, Philipp; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).

[ BibTeX ] [ EndNote ]

Integrating Docker into the BUNGEE cloud elasticity benchmark. Roos, Filipp; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).

[ BibTeX ] [ EndNote ]

Cross-Platform Evaluation of Performance Event Trigger. Hegemann, Tim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, June).

[ BibTeX ] [ EndNote ]

Automatic Identification of Parametric Dependencies for Performance Models. Elflein, Sven; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).

Würzburg Software Engineering Award sponsored by Bosch Rexroth

[ BibTeX ] [ EndNote ]

An Evaluation and Benchmarking Solution for Security Information and Event Management Systemes. Ben Yahya, Ala Eddine; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, March).

[ BibTeX ] [ EndNote ]

DIMAQS - Dynamic Identiﬁcation of Malicious Query Sequences. Jobst, Michael; Thesis; University of Würzburg. (2018, June).

[ Abstract ] [ BibTeX ] [ EndNote ]

@mastersthesis{jobst2018dimaqs,
  abstract = {Ransomware is an emerging threat which imposed 5 billion USD loss in 2017 and is predicted to hit 11.5 billion in 2019. While initially targeting PC (client) platforms,recently it made a leap to server-side databases – in January 2017 we faced MongoDB Apocalypse attack, followed by other attack waves targeting a wide range of DB technologies such as MongoDB, MySQL, ElasticSearch, Cassandra, Hadoop, and CouchDB. While previous research has developed countermeasures against client-side ransomware (e.g., CryptoDrop and ShieldFS), no attention was given to the problem of server-side ransomware yet.This thesis aims to bridge this gap and presents design and implementation of the tool DIMAQS (Dynamic Identification of Malicious Query Sequences) for MySQL servers that can efficiently and effectively detect server-side ransomware. DIMAQS performs run-time monitoring of incoming queries and pattern matching using a Colored Petri Net (CPN) for attack detection. The system design of DIMAQS exhibits several novel techniques to enable efficient detection of malicious query sequences globally (i.e., without limiting detection to distinct user connections). Evaluation results show high efficiency with no false positives and no false negatives,and a very moderate performance overhead (for a run-time monitoring tool) of under5% in worst case scenarios.},
  author = {Jobst, Michael},
  keywords = {international-conference-workshop-papers-book-chapters journal-and-magazine-articles sss-group thesis_supervised_by_SSS_member thesis_supervised_by_SE_member},
  month = {June},
  school = {University of W{ü}rzburg},
  title = {DIMAQS - Dynamic Identiﬁcation of Malicious Query Sequences},
  type = {Master Thesis},
  year = 2018
}

Comparing Machine Learning Approaches for Multivariate Time Series Forecasting. Otto, Dominik; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, April).

[ BibTeX ] [ EndNote ]

Efficiency Increase of Servers through Fan Control via Software. Knaub, Christian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, March).

[ BibTeX ] [ EndNote ]

Blackbox Learning of Parametric Dependencies for Performance Models from Monitoring Data. Ackermann, Vanessa; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, March).

[ BibTeX ] [ EndNote ]

Time Constrained Trend and Season Detection for Load Intensity Profiles. Heilmann, Benno; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018).

[ BibTeX ] [ EndNote ]

Entwicklung einer dynamischen Webanwendung für Forecast Benchmarking. Cramer, Julia; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, September).

[ BibTeX ] [ EndNote ]

Performance and Security Influence of Augmenting DDoS Protection Systems using SDN and NFV. Beierlieb, Lukas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, January).

Würzburg Software Engineering Award sponsored by Infosim GmbH & Co. KG

[ BibTeX ] [ EndNote ] [ Download ]

Hardening Bitcoin Against Off-Topic Data Inclusion Attacks. Seeg, Andreas; Thesis; University of Würzburg. (2018, August).

[ Abstract ] [ BibTeX ] [ EndNote ]

Bitcoin is a novel peer to peer payment network that was started in 2009 and that has since flourished, gaining acceptance all over the world. By contributing their resourcefulness as network connectivity and computing power, all participants are creating the the Bitcoin network together. Designed as an open source software system, the participation rules for this network are not upheld by humans, but implemented in code, and generally allow anyone to join or leave the network at will.Since the time of Bitcoin’s inception, software errors had to be fixed and network attacks that tried to make parts of the network unavailable have been endured. This thesis provides a solution to the problem of illegal data inclusions within the blockchain, a data structure that cannot easily be changed and that has to be transmitted to participants of Bitcoin.The flaw, i.e. the possibility for any user of Bitcoin to potentially include illegal data and therefore illegalizes the whole blockchain, is not just a software flaw, but can be seen as a structural one if free participation and uncensorable payments are to be provided.The proposal to solve the problem is layered in two tiers. The first tier prevents effective data inclusion for some standard payment functions within Bitcoin. This allows participants to be certain that these transactions do not contain bad data, allowing them to be included into Bitcoin blocks. It also solves the problem of illegal data permanently occupying valuable resources on Bitcoin nodes, as parts of the provably clean transactions are generally held in RAM until they have been spent. The prevention mechanism is essentially a system that provides proof that bitcoins were sent to actual Bitcoin addresses,stopping data inclusion by address fields abusal.The second tier consists of rule changes for Bitcoin. First of all, all non-standard payment transactions are removed from Bitcoin, with the exception of the more computationally flexible and popular P2SH transactions. This limits the avenues for data inclusion in Bitcoin without destroying its flexibility or its smart contract capabilities. Then, P2SHtransactions are redesigned to allow for a more fine grained deletion of their data, so only offending parts could be removed without destroying the integrity of the rest. Finally, a special type of transaction is introduced that allows censorship of just the signature data of a P2SH transaction. This transaction has to include a payment equal to the Bitcoin provided by the previous P2SH transaction, however. By adding this requirement, the system is protected from becoming open to money creation attacks that might inflate the currency without the agreement of all participants.With these changes, illegal data inclusion becomes either impossible, or they can be re-moved without destroying the integrity of the payment system as a whole or for payments that have already happened.Implementing all proposed changes would go beyond the scope of a master thesis. Tier1 of the presented solution has been implemented as a proof-of-concept, though. As a basis, the most popular Bitcoin node software, Bitcoin Core, has been used. Additionally,the ideas behind tier 2 are explained in full, and the potential impact on the system is discussed.v This thesis also covers previous attempts to allow for data removal in similar systems. Their discussion is meant to encourage the adoption of the presented proposal, as it should causeless disruption than some more extreme measurements that might introduce centralization into a system that was meant to run decentralized.

@mastersthesis{seeg2018hardening,
  abstract = {Bitcoin is a novel peer to peer payment network that was started in 2009 and that has since flourished, gaining acceptance all over the world. By contributing their resourcefulness as network connectivity and computing power, all participants are creating the the Bitcoin network together. Designed as an open source software system, the participation rules for this network are not upheld by humans, but implemented in code, and generally allow anyone to join or leave the network at will.Since the time of Bitcoin’s inception, software errors had to be fixed and network attacks that tried to make parts of the network unavailable have been endured. This thesis provides a solution to the problem of illegal data inclusions within the blockchain, a data structure that cannot easily be changed and that has to be transmitted to participants of Bitcoin.The flaw, i.e. the possibility for any user of Bitcoin to potentially include illegal data and therefore illegalizes the whole blockchain, is not just a software flaw, but can be seen as a structural one if free participation and uncensorable payments are to be provided.The proposal to solve the problem is layered in two tiers. The first tier prevents effective data inclusion for some standard payment functions within Bitcoin. This allows participants to be certain that these transactions do not contain bad data, allowing them to be included into Bitcoin blocks. It also solves the problem of illegal data permanently occupying valuable resources on Bitcoin nodes, as parts of the provably clean transactions are generally held in RAM until they have been spent. The prevention mechanism is essentially a system that provides proof that bitcoins were sent to actual Bitcoin addresses,stopping data inclusion by address fields abusal.The second tier consists of rule changes for Bitcoin. First of all, all non-standard payment transactions are removed from Bitcoin, with the exception of the more computationally flexible and popular P2SH transactions. This limits the avenues for data inclusion in Bitcoin without destroying its flexibility or its smart contract capabilities. Then, P2SHtransactions are redesigned to allow for a more fine grained deletion of their data, so only offending parts could be removed without destroying the integrity of the rest. Finally, a special type of transaction is introduced that allows censorship of just the signature data of a P2SH transaction. This transaction has to include a payment equal to the Bitcoin provided by the previous P2SH transaction, however. By adding this requirement, the system is protected from becoming open to money creation attacks that might inflate the currency without the agreement of all participants.With these changes, illegal data inclusion becomes either impossible, or they can be re-moved without destroying the integrity of the payment system as a whole or for payments that have already happened.Implementing all proposed changes would go beyond the scope of a master thesis. Tier1 of the presented solution has been implemented as a proof-of-concept, though. As a basis, the most popular Bitcoin node software, Bitcoin Core, has been used. Additionally,the ideas behind tier 2 are explained in full, and the potential impact on the system is discussed.v This thesis also covers previous attempts to allow for data removal in similar systems. Their discussion is meant to encourage the adoption of the presented proposal, as it should causeless disruption than some more extreme measurements that might introduce centralization into a system that was meant to run decentralized.},
  author = {Seeg, Andreas},
  keywords = {attacks international-conference-workshop-papers-book-chapters journal-and-magazine-articles sss-group technical-reports thesis_supervised_by_SSS_member thesis_supervised_by_SE_member},
  month = {aug},
  school = {University of W{ü}rzburg},
  title = {Hardening Bitcoin Against Off-Topic Data Inclusion Attacks},
  type = {Master Thesis},
  year = 2018
}

%0 Thesis
%1 seeg2018hardening
%A Seeg, Andreas
%D 2018
%T Hardening Bitcoin Against Off-Topic Data Inclusion Attacks
%X Bitcoin is a novel peer to peer payment network that was started in 2009 and that has since flourished, gaining acceptance all over the world. By contributing their resourcefulness as network connectivity and computing power, all participants are creating the the Bitcoin network together. Designed as an open source software system, the participation rules for this network are not upheld by humans, but implemented in code, and generally allow anyone to join or leave the network at will.Since the time of Bitcoin’s inception, software errors had to be fixed and network attacks that tried to make parts of the network unavailable have been endured. This thesis provides a solution to the problem of illegal data inclusions within the blockchain, a data structure that cannot easily be changed and that has to be transmitted to participants of Bitcoin.The flaw, i.e. the possibility for any user of Bitcoin to potentially include illegal data and therefore illegalizes the whole blockchain, is not just a software flaw, but can be seen as a structural one if free participation and uncensorable payments are to be provided.The proposal to solve the problem is layered in two tiers. The first tier prevents effective data inclusion for some standard payment functions within Bitcoin. This allows participants to be certain that these transactions do not contain bad data, allowing them to be included into Bitcoin blocks. It also solves the problem of illegal data permanently occupying valuable resources on Bitcoin nodes, as parts of the provably clean transactions are generally held in RAM until they have been spent. The prevention mechanism is essentially a system that provides proof that bitcoins were sent to actual Bitcoin addresses,stopping data inclusion by address fields abusal.The second tier consists of rule changes for Bitcoin. First of all, all non-standard payment transactions are removed from Bitcoin, with the exception of the more computationally flexible and popular P2SH transactions. This limits the avenues for data inclusion in Bitcoin without destroying its flexibility or its smart contract capabilities. Then, P2SHtransactions are redesigned to allow for a more fine grained deletion of their data, so only offending parts could be removed without destroying the integrity of the rest. Finally, a special type of transaction is introduced that allows censorship of just the signature data of a P2SH transaction. This transaction has to include a payment equal to the Bitcoin provided by the previous P2SH transaction, however. By adding this requirement, the system is protected from becoming open to money creation attacks that might inflate the currency without the agreement of all participants.With these changes, illegal data inclusion becomes either impossible, or they can be re-moved without destroying the integrity of the payment system as a whole or for payments that have already happened.Implementing all proposed changes would go beyond the scope of a master thesis. Tier1 of the presented solution has been implemented as a proof-of-concept, though. As a basis, the most popular Bitcoin node software, Bitcoin Core, has been used. Additionally,the ideas behind tier 2 are explained in full, and the potential impact on the system is discussed.v This thesis also covers previous attempts to allow for data removal in similar systems. Their discussion is meant to encourage the adoption of the presented proposal, as it should causeless disruption than some more extreme measurements that might introduce centralization into a system that was meant to run decentralized.

2017 [ nach oben ]

Runtime Prediction of Power Consumption for Server Reconfigurations. Deffner, Maximilian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, May).

[ BibTeX ] [ EndNote ]

Solving Explicit Dependencies for Architectural Performance Models. Eismann, Simon; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, March).

ASQF Award

[ BibTeX ] [ EndNote ] [ Download ]

Dynamic Hybrid Forecasting for Self-Aware Systems. Züfle, Marwin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, October).

Würzburg Software Engineering Award sponsored by infosim

[ BibTeX ] [ EndNote ] [ Download ]

Self-Aware Multidimensional Auto-Scaling. Lesch, Veronika; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, September).

Würzburg Software Engineering Award sponsored by Bosch Rexroth

[ BibTeX ] [ EndNote ] [ Download ]

Cybersecurity Risk Exposure: Towards a Novel Determination Approach based on OSINT. Leonhardt, Alexander; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, October).

[ BibTeX ] [ EndNote ]

Evaluation der Performanz von IDSes in virtualisierten Netzwerk-Infrastrukturen. Stoll, Jonathan; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, June).

[ BibTeX ] [ EndNote ]

A Packet-Filtering Firewall for Cloud Computing. Hempfling, Christina; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, September).

[ BibTeX ] [ EndNote ]

A Framework for a Dynamic Function-Chaining-Controller for Virtualized Network Security Appliances. Ciner, Hayreddin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, October).

[ BibTeX ] [ EndNote ]

Elasticity Benchmarking for Multi-Tier Cloud Applications. Wilhelm, Marcus; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, June).

Würzburg Software Engineering Award sponsored by Bosch Rexroth

[ BibTeX ] [ EndNote ]

Evaluating Approaches for Online Resource Demand Estimation. Krauss, Torsten; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, September).

[ BibTeX ] [ EndNote ]

Cost Estimation of Performance Analysis Approaches. Beltran, Cristina Llamas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, April).

[ BibTeX ] [ EndNote ]

Cluster Analysis for Resource Demand Estimation. Schaefers, Karsten; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2017, August).

[ BibTeX ] [ EndNote ]

2016 [ nach oben ]

Fluid Analysis of Queueing Petri Nets Models using Model-to-Model Transformations. Müller, Christoph; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, April).

[ BibTeX ] [ EndNote ]

P-Seminar: Raspberry Pi als Mediencenter für die Oberstufe. Frauendorfer, Katja; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, April).

[ BibTeX ] [ EndNote ]

Messung und Analyse des Stromverbrauchs von Speicherzugriffsmustern. Agne, Joachim; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, November).

[ BibTeX ] [ EndNote ]

Reliable Resource Demand Estimation. Grohmann, Johannes; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, October).

[ Abstract ] [ BibTeX ] [ EndNote ]

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{ä}ude, 97074 W{ü}rzburg, Germany},
  author = {Grohmann, Johannes},
  keywords = {Instrumentation_profiling_and_workload_characterization LibReDE Multi-criteria_optimization Optimization Resource_management Self-adaptive-systems Statistical_estimation_and_machine_learning Supervised_by_Nikolas_Herbst Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Spinner Tool descartes se2_mastersthesis t_studentthesis},
  month = {October},
  school = {University of W{ü}rzburg},
  title = {{Reliable Resource Demand Estimation}},
  type = {{Master Thesis}},
  year = 2016
}

%0 Thesis
%1 grohmann16
%A Grohmann, Johannes
%C Am Hubland, Informatikgebäude, 97074 Würzburg, 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.

Modeling Server Energy Consumption using Machine Learning. Oestreicher, Tobias; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, October).

[ BibTeX ] [ EndNote ]

Transformation of Descartes Modeling Language to Queueing Networks. Dietz, Katharina; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, September).

[ Abstract ] [ BibTeX ] [ EndNote ]

Result Visualization for Performance Analyses. König, Maximilian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, August).

[ BibTeX ] [ EndNote ]

Extending the Descartes Query Language to Support Automated What-If Analysis. Knapp, Andreas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, August).

[ Abstract ] [ BibTeX ] [ EndNote ]

A Capability Model for Solution Approaches in Performance Engineering. Thiele, Christoph; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, March).

[ BibTeX ] [ EndNote ]

Modeling SDN Performance with Queueing Petri Nets using Model-to-Model Transformations. Kiesner, Maximilian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, September).

[ BibTeX ] [ EndNote ]

Assessment of the Influence of I/O-Load Factors on Service Migration Performance. Liu, Xiaofen; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, December).

[ BibTeX ] [ EndNote ]

Multi-Cloud IaaS Modeling for Interoperability. König, Karsten Frederik; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, November).

[ BibTeX ] [ EndNote ]

Self-Learning Forecast Method Selection. Smolka, Joshua; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, November).

[ Abstract ] [ BibTeX ] [ EndNote ]

Which weather will it be tomorrow? Which product will be most purchased next month? When do you have to order additional material to guarantee your production process? How will your stock value develop? In many areas, forecast methods are used to estimate future developments. Many different models have been developed over the years, each of them having their own strengths and weaknesses. An efficient selection of an appropriate method is therefore a complex process that requires expert knowledge and experience. In times of Big Data - going along with increasing application possibilities of forecasting - more and more cases occur with limited expertise. Along with the commonly applied trial-and-error procedure, this leads to inefficient and time-consuming operational processes. A convenient solution to this problem is the approach to automate the forecast method selection. Therefore, a machine-learning algorithm is used to generate a ruleset which recommends a well-performing method based on the characteristics of a time series. Several works exist on the topic of automated rule generation for forecast method selection. We extend these by considering additional forecast methods and different machine-learning techniques. Also, we emphasise on an elaborate evaluation, which many of the previous work lack. Furthermore, it is planned to provide the program code under an open source licence, giving future work the opportunity to base on this thesis. First, the characteristics of the training data are calculated. Afterwards, we analyse which methods deliver good forecasts on which datasets. Based on these results, we use the machine-learning approaches to generate the rulesets that are later used for forecast method recommendation. Then, we evaluate the different results by testing them on new datasets. Additionally, we measure the runtime of each approach and analyse the reduction of the computing time. The results show that the recommendation quality of all approaches is slightly, but not convincingly better than a random choice of forecast method. The runtime of all approaches are signicantly lower compared to running all methods simultaneously.

@mastersthesis{smolka16,
  abstract = {Which weather will it be tomorrow? Which product will be most purchased next month? When do you have to order additional material to guarantee your production process? How will your stock value develop? In many areas, forecast methods are used to estimate future developments. Many different models have been developed over the years, each of them having their own strengths and weaknesses. An efficient selection of an appropriate method is therefore a complex process that requires expert knowledge and experience. In times of Big Data - going along with increasing application possibilities of forecasting - more and more cases occur with limited expertise. Along with the commonly applied trial-and-error procedure, this leads to inefficient and time-consuming operational processes. A convenient solution to this problem is the approach to automate the forecast method selection. Therefore, a machine-learning algorithm is used to generate a ruleset which recommends a well-performing method based on the characteristics of a time series. Several works exist on the topic of automated rule generation for forecast method selection. We extend these by considering additional forecast methods and different machine-learning techniques. Also, we emphasise on an elaborate evaluation, which many of the previous work lack. Furthermore, it is planned to provide the program code under an open source licence, giving future work the opportunity to base on this thesis. First, the characteristics of the training data are calculated. Afterwards, we analyse which methods deliver good forecasts on which datasets. Based on these results, we use the machine-learning approaches to generate the rulesets that are later used for forecast method recommendation. Then, we evaluate the different results by testing them on new datasets. Additionally, we measure the runtime of each approach and analyse the reduction of the computing time. The results show that the recommendation quality of all approaches is slightly, but not convincingly better than a random choice of forecast method. The runtime of all approaches are signicantly lower compared to running all methods simultaneously.},
  address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany},
  author = {Smolka, Joshua},
  keywords = {Online_monitoring_and_forecasting se2_mastersthesis descartes Thesis_supervised_by_SE_member WCF Supervised_by_Nikolas_Herbst},
  month = {November},
  school = {University of W{ü}rzburg},
  title = {{Self-Learning Forecast Method Selection}},
  type = {{Master Thesis}},
  year = 2016
}

%0 Thesis
%1 smolka16
%A Smolka, Joshua
%C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany
%D 2016
%T Self-Learning Forecast Method Selection
%X Which weather will it be tomorrow? Which product will be most purchased next month? When do you have to order additional material to guarantee your production process? How will your stock value develop? In many areas, forecast methods are used to estimate future developments. Many different models have been developed over the years, each of them having their own strengths and weaknesses. An efficient selection of an appropriate method is therefore a complex process that requires expert knowledge and experience. In times of Big Data - going along with increasing application possibilities of forecasting - more and more cases occur with limited expertise. Along with the commonly applied trial-and-error procedure, this leads to inefficient and time-consuming operational processes. A convenient solution to this problem is the approach to automate the forecast method selection. Therefore, a machine-learning algorithm is used to generate a ruleset which recommends a well-performing method based on the characteristics of a time series. Several works exist on the topic of automated rule generation for forecast method selection. We extend these by considering additional forecast methods and different machine-learning techniques. Also, we emphasise on an elaborate evaluation, which many of the previous work lack. Furthermore, it is planned to provide the program code under an open source licence, giving future work the opportunity to base on this thesis. First, the characteristics of the training data are calculated. Afterwards, we analyse which methods deliver good forecasts on which datasets. Based on these results, we use the machine-learning approaches to generate the rulesets that are later used for forecast method recommendation. Then, we evaluate the different results by testing them on new datasets. Additionally, we measure the runtime of each approach and analyse the reduction of the computing time. The results show that the recommendation quality of all approaches is slightly, but not convincingly better than a random choice of forecast method. The runtime of all approaches are signicantly lower compared to running all methods simultaneously.

Cyber-Physical Systems - Models and Security Aspects. Hempfling, Christina; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, December).

[ BibTeX ] [ EndNote ]

Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. Bauer, André; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016).

[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]

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{ä}ude, 97074 W{ü}rzburg, Germany},
  author = {Bauer, Andr{é}},
  keywords = {Analytical_and_simulation-based_analysis Application-aware BUNGEE Chameleon Cloud DML Elasticity Instrumentation_profiling_and_workload_characterization LIMBO LibReDE Metrics_and_benchmarking_methodologies Online_monitoring_and_forecasting Performance Power-energy_efficient_computing Prediction Resource_management SPEC Self-adaptive-systems Self-aware-computing Statistical_estimation_and_machine_learning Supervised_by_Nikolas_Herbst Thesis_supervised_by_SE_member Thesis_supervised_by_Simon_Spinner Virtualization WCF descartes se2_mastersthesis t_studentthesis},
  month = {Septembter},
  school = {University of W{ü}rzburg},
  title = {{Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism}},
  type = {{Master Thesis}},
  year = 2016
}

%0 Thesis
%1 Bauer2016
%A Bauer, André
%C Am Hubland, Informatikgebäude, 97074 Würzburg, 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.

Local Approximation of NFV Workloads. Schmitt, Norbert; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, October).

[ BibTeX ] [ EndNote ] [ Download ]

SDN-basierte Lastverteilung für Schicht-7 Anfragen (SDN Rechenzentrum Fallstudie). Stoll, Jonathan; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2016, March).

[ BibTeX ] [ EndNote ]

2015 [ nach oben ]

Declarative Automation Framework for QPME based on DQL. Eismann, Simon; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, March).

[ BibTeX ] [ EndNote ] [ Download ]

Automated Transformation of Descartes Modeling Language to Palladio Component Models. Hildebrandt, Adrian; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, January).

[ BibTeX ] [ EndNote ]

Compiling Handlebars to C#. Demler, Jakob; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, December).

[ BibTeX ] [ EndNote ]

Benchmarking Energy Efficiency of Servers in Virtualized Environments. Schreck, Marco; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015, December).

[ BibTeX ] [ EndNote ]

Quantification of the Impact of Transient Behaviors of Intrusion Detection Systems on Evaluation Metrics. Leonhardt, Alexander; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2015).

[ BibTeX ] [ EndNote ]

2014 [ nach oben ]

Metriken zur Bestimmung der Energieeffizienz von Servern. Gehring, Andreas; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2014, October).

[ BibTeX ] [ EndNote ]

Resource Elasticity Benchmarking in Cloud Environments. Weber, Andreas; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2014, August).

[ Abstract ] [ BibTeX ] [ EndNote ]

Auto-scaling features offered by today's cloud infrastructures provide increased flexibility, especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy and timing. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. This thesis discusses the shortcomings of existing approaches for measuring and evaluating elastic behavior and proposes a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmarking concept uses open workloads with realistic load intensity profiles in order to induce resource demand variations on the benchmarked system and compares them with the actual variation of the allocated resources. To ensure a fair elasticity comparison between systems with di erent underlying hardware performance, the load intensity profiles are adjusted to induce identical resource demand variations on all compared platforms. Furthermore, this thesis proposes new metrics that capture the accuracy of resource allocations and deallocations, as well as the timing aspects of an auto-scaling mechanism, explicitly. The benchmark concept comprises four activities: The System Analysis evaluates the load processing capabilities of the benchmarked platform for different scaling stages. The Benchmark Calibration then uses the analysis results and adjusts a given load intensity profile in a system specific manner. Within the Measurement activity, the evaluated platformis exposed to a load varying ccording to the adjusted intensity profile. The final Elasticity Evaluation measures the quality of the observed elastic behavior using the proposed elasticity metrics. A java based framework for benchmarking the elasticity of IaaS cloud platforms called BUNGEE implements this concept and automates benchmarking activities. At the moment, BUNGEE allows to analyze the elasticity of CloudStack and Amazon Web Service (AWS) based clouds that scale CPU-bound virtual machines horizontally. Within an extensive evaluation, this thesis demonstrates the ability of the proposed elasticity metrics to consistently rank elastic systems on an ordinal scale. A case study that uses a realistic load profile, consisting of several millions of request submissions, exhibits the applicability of the benchmarking methodology for realistic scenarios. The case study is conducted on a private as well as on a public cloud and uses eleven different elasticity rule configurations and four instance types assigned to resources with different levels of effciency.

@mastersthesis{Weber2014,
  abstract = {{Auto-scaling features offered by today's cloud infrastructures provide increased flexibility, especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy and timing. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. This thesis discusses the shortcomings of existing approaches for measuring and evaluating elastic behavior and proposes a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmarking concept uses open workloads with realistic load intensity profiles in order to induce resource demand variations on the benchmarked system and compares them with the actual variation of the allocated resources. To ensure a fair elasticity comparison between systems with di erent underlying hardware performance, the load intensity profiles are adjusted to induce identical resource demand variations on all compared platforms. Furthermore, this thesis proposes new metrics that capture the accuracy of resource allocations and deallocations, as well as the timing aspects of an auto-scaling mechanism, explicitly. The benchmark concept comprises four activities: The System Analysis evaluates the load processing capabilities of the benchmarked platform for different scaling stages. The Benchmark Calibration then uses the analysis results and adjusts a given load intensity profile in a system specific manner. Within the Measurement activity, the evaluated platformis exposed to a load varying ccording to the adjusted intensity profile. The final Elasticity Evaluation measures the quality of the observed elastic behavior using the proposed elasticity metrics. A java based framework for benchmarking the elasticity of IaaS cloud platforms called BUNGEE implements this concept and automates benchmarking activities. At the moment, BUNGEE allows to analyze the elasticity of CloudStack and Amazon Web Service (AWS) based clouds that scale CPU-bound virtual machines horizontally. Within an extensive evaluation, this thesis demonstrates the ability of the proposed elasticity metrics to consistently rank elastic systems on an ordinal scale. A case study that uses a realistic load profile, consisting of several millions of request submissions, exhibits the applicability of the benchmarking methodology for realistic scenarios. The case study is conducted on a private as well as on a public cloud and uses eleven different elasticity rule configurations and four instance types assigned to resources with different levels of effciency.}},
  address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany},
  author = {Weber, Andreas},
  keywords = {Metrics_and_benchmarking_methodologies Self-adaptive-systems LIMBO Performance Virtualization BUNGEE Supervised_by_Nikolas_Herbst Cloud descartes Thesis_supervised_by_SE_member Tool Elasticity se2_mastersthesis},
  month = {August},
  school = {{Karlsruhe Institute of Technology (KIT)}},
  title = {{Resource Elasticity Benchmarking in Cloud Environments}},
  type = {{Master Thesis}},
  year = 2014
}

%0 Thesis
%1 Weber2014
%A Weber, Andreas
%C Am Fasanengarten 5, 76131 Karlsruhe, Germany
%D 2014
%T Resource Elasticity Benchmarking in Cloud Environments
%X Auto-scaling features offered by today's cloud infrastructures provide increased flexibility, especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy and timing. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. This thesis discusses the shortcomings of existing approaches for measuring and evaluating elastic behavior and proposes a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmarking concept uses open workloads with realistic load intensity profiles in order to induce resource demand variations on the benchmarked system and compares them with the actual variation of the allocated resources. To ensure a fair elasticity comparison between systems with di erent underlying hardware performance, the load intensity profiles are adjusted to induce identical resource demand variations on all compared platforms. Furthermore, this thesis proposes new metrics that capture the accuracy of resource allocations and deallocations, as well as the timing aspects of an auto-scaling mechanism, explicitly. The benchmark concept comprises four activities: The System Analysis evaluates the load processing capabilities of the benchmarked platform for different scaling stages. The Benchmark Calibration then uses the analysis results and adjusts a given load intensity profile in a system specific manner. Within the Measurement activity, the evaluated platformis exposed to a load varying ccording to the adjusted intensity profile. The final Elasticity Evaluation measures the quality of the observed elastic behavior using the proposed elasticity metrics. A java based framework for benchmarking the elasticity of IaaS cloud platforms called BUNGEE implements this concept and automates benchmarking activities. At the moment, BUNGEE allows to analyze the elasticity of CloudStack and Amazon Web Service (AWS) based clouds that scale CPU-bound virtual machines horizontally. Within an extensive evaluation, this thesis demonstrates the ability of the proposed elasticity metrics to consistently rank elastic systems on an ordinal scale. A case study that uses a realistic load profile, consisting of several millions of request submissions, exhibits the applicability of the benchmarking methodology for realistic scenarios. The case study is conducted on a private as well as on a public cloud and uses eleven different elasticity rule configurations and four instance types assigned to resources with different levels of effciency.

Modeling Variations in Load Intensity Profiles. von Kistowski, Jóakim; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2014, March).

Gesellschaft zur Förderung des Forschungstransfers (GFFT) Prize "Best Master Thesis"

[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]

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{ó}akim},
  keywords = {Automated_model_learning Award Cloud Instrumentation_profiling_and_workload_characterization LIMBO Meta-models Supervised_by_Nikolas_Herbst Thesis_supervised_by_SE_member Tool descartes t_studentthesis},
  month = {March},
  note = {<b>Gesellschaft zur F{ö}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, Jóakim
%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.

2013 [ nach oben ]

Überwachung des effektiven Kontrollflusses einer mehrschichtigen Anwendung. Yavuz, Can Alexander; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2013, April).

[ BibTeX ] [ EndNote ]

Online Performance Queries for Architecture-Level Performance Models. Gorsler, Fabian; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2013, July).

[ BibTeX ] [ EndNote ] [ Download ]

Parallel Simulation of Queueing Petri Net Models. Walter, Jürgen; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2013, October).

[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]

@mastersthesis{Walter2013-parallel-simulation,
  abstract = {For years the CPU clock frequency was the key to improve processor performance. Nowadays, modern processors enable performance improvements by increasing the number of cores. However, existing software needs to be adapted to be able to utilize multiple cores. Such an adaptation poses many challenges in the field of discrete-event software simulation. Decades of intensive research have been spent to find a general solution for parallel discrete event simulation. In this context, QNs and PNs have been extensively studied. However, to the best of our knowledge, there is only one previous work that considers the concurrent simulation of QPN [Juergens1997]. This work focuses on comparing different synchronization algorithms and excludes a majority of lookahead calculation and net decomposition. In this thesis, we build upon and extend this work. For this purpose, we adapted and extended findings from QNs, PNs and parallel simulation in general. We apply our findings to SimQPN, which is a sequential simulation engine for QPN. Among other application areas, SimQPN is currently applied to online performance prediction for which a speedup due to parallelization is desirable. We present a parallel SimQPN implementation that employs application level and event level parallelism. A validation ensures the functional correctness of the new parallel implementations. The parallelization of multiple runs enables almost linear speedup. We parallelized the execution of a single run by the use of a conservative barrier-based synchronization algorithm. The speedup for a single run depends on the capability of the model. Hence, a number of experiments on different net characteristics were conducted showing that for certain models a superlinear speedup is possible.},
  address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany},
  author = {Walter, J{ü}rgen},
  keywords = {Thesis_supervised_by_Simon_Spinner Thesis_supervised_by_SE_member QPME Performance se2_mastersthesis Simulation descartes QPME_Bibliography Tool QPN Analytical_and_simulation-based_analysis},
  month = {October},
  school = {Karlsruhe Institute of Technology (KIT)},
  title = {{Parallel Simulation of Queueing Petri Net Models}},
  type = {Master Thesis},
  year = 2013
}

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

Performance-Vorhersage von virtualisiertem Storage mit Warteschlangennetzen. Rostami, Kiana; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2013).

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Workload Characterization for I/O Performance Analysis on IBM System z. Busch, Axel; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2013).

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Automated Model-based System Reconfiguration: A Case Study. Schott, Waldemar; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2013).

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2012 [ nach oben ]

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 ] [ BibTeX ] [ EndNote ] [ Download ]

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 = {Award Cloud Online_monitoring_and_forecasting Prediction Resource_management Statistical_estimation_and_machine_learning Thesis_supervised_by_Nikolaus_Huber Thesis_supervised_by_SE_member Tool WCF descartes se2_mastersthesis 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.

Infrastructure Reconfiguration Using Architecture-Level Performance Models. Rattu, Dharminder; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2012).

[ BibTeX ] [ EndNote ]

Verbesserte Performance-Vorhersage für virtualisierte Umgebungen durchIntegration von Messergebnissen. Schott, Waldemar; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2012).

(in German)

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Modeling and Experimental Analysis of Virtualized Storage Performance using IBM System Z as Example. Bruhn, Dominik; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2012).

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2011 [ nach oben ]

QoS-aware Resource Allocation in Virtualized Environments. Kusnady, Enrico; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2011).

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Performance-Modellierung des 1&1 Mail-Systems. Becker, Stefan; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2011).

[ BibTeX ] [ EndNote ]

Evaluating Approaches to Resource Demand Estimation. Spinner, Simon; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2011, July).

Best Graduate Award from the Faculty of Informatics

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@mastersthesis{Spinner2011a,
  abstract = {Proactively managing the performance and resource efficiency of running software systems requires techniques to predict system performance and resource consumption. Typically, performance predictions are based on performance models that capture the performance-relevant aspects of the considered software system. Building performance models involves the estimation of resource demands, i.e., estimating the time a unit of work spends obtaining service from a resource. A number of approaches to estimating the resource demands of a system already exist, e.g., based on regression analysis or stochastic filtering. These approaches differ in their accuracy, their robustness and their applicability. For instance, there are notable differences in the amount and type of measurement data that is required as input. However, to the best of our knowledge, a comprehensive evaluation and comparison of these approaches in a representative context does not exist. In this thesis, we give an overview of the state-of-the-art in resource demand estimation and develop a classification scheme for approaches to resource demand estimation. We implement a sub-set of these estimation approaches and evaluate them in a representative environment. We analyze the influence of various factors of the environment on the estimation accuracy, considering the impact of current technologies, such as multi-core processors and virtualization. Our work improves the comparability of existing estimation approaches and facilitates the selection of an approach in a given application scenario. Additionally, it shows possible directions for future research in the field of resource demand estimation.},
  address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany},
  author = {Spinner, Simon},
  keywords = {Automated_model_learning LibReDE Thesis_supervised_by_SE_member Performance Thesis_supervised_by_Fabian_Brosig descartes QPME_Bibliography Statistical_estimation_and_machine_learning se2_mastersthesis},
  month = 7,
  note = {Best Graduate Award from the Faculty of Informatics},
  school = {Karlsruhe Institute of Technology (KIT)},
  title = {{Evaluating Approaches to Resource Demand Estimation}},
  year = 2011
}

2010 [ nach oben ]

Comparison of selected SOA middleware platforms focusing on provided facilities for online monitoring, instrumentation and dynamic reconfiguration. Matershev, Evgeny; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2010).

[ BibTeX ] [ EndNote ]

Automatisierte Performance-Analyse von Virtualisierungsplattformen. von Quast, Marcel; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2010).

[ BibTeX ] [ EndNote ]

Automated Transformation of Palladio Component Models to Queueing Petri Nets. Meier, Philipp; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2010).

FZI Prize "Best Diploma Thesis"

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Automated Performance Model Extraction of Enterprise Data Fabrics. Bender, Konstantin; Thesis; Karlsruhe Institute of Technology, Karlsruhe, Germany; Karlsruhe, Germany. (2010, May).

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2009 [ nach oben ]

Querying, Aggregating and Visualizing QPN Model Analysis Data. Zipp, Frederik; Thesis; Karlsruhe Institute of Technology (KIT); Karlsruhe, Germany. (2009).

[ BibTeX ] [ EndNote ]

Performance Modeling of Storage Virtualization. Huber, Nikolaus; Thesis; Universität Karlsruhe (TH); Karlsruhe, Germany. (2009, April).

GFFT Prize

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Automated Extraction of Palladio Component Models from Running Enterprise Java Applications. Brosig, Fabian; Thesis; Universität Karlsruhe (TH); Karlsruhe, Germany. (2009, June).

FZI Prize "Best Diploma Thesis"

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Supervised student theses

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