Publications

Dissertation oder Abschlussarbeit [ nach oben ]

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 ]
 
@mastersthesis{Lesch2017Self-Aware, address = {Am Hubland, Informatikgeb{ä}ude, 97074 W{ü}rzburg, Germany}, author = {Lesch, Veronika}, keywords = {Thesis_supervised_by_SE_member myown Resource_management t_studentthesis Performance descartes Thesis_supervised_by_Andre_Bauer Elasticity Chameleon se2_mastersthesis Self-aware-computing Cloud BUNGEE Supervised_by_Nikolas_Herbst Application_quality_of_service_management Virtualization}, month = {September}, note = {<b>W{ü}rzburg Software Engineering Award sponsored by Bosch Rexroth</b>}, school = {University of W{ü}rzburg}, title = {{Self-Aware Multidimensional Auto-Scaling}}, type = {{Master Thesis}}, year = 2017 }
%0 Thesis %1 Lesch2017Self-Aware %A Lesch, Veronika %C Am Hubland, Informatikgebäude, 97074 Würzburg, Germany %D 2017 %T Self-Aware Multidimensional Auto-Scaling

Artikel in einem Journal, einer Zeitung oder einem Magazin [ nach oben ]

Towards Self-Aware Multirotor Formations. Kaiser, Dennis; Lesch, Veronika; Rothe, Julian; Strohmeier, Michael; Spiess, Florian; Krupitzer, Christian; Montenegro, Sergio; Kounev, Samuel; in Computers (2020). 9(7)
[ BibTeX ] [ EndNote ] [ Download ]
 
@article{lesch2020comparison, author = {Lesch, Veronika and Krupitzer, Christian and Stubenrauch, Kevin and Keil, Nico and Becker, Christian and Kounev, Samuel and Segata, Michele}, journal = {Future Generation Computer Systems}, keywords = {myown select descartes intelligent_transportation_systems cyber-physical_systems internet_of_things compensation system_integration t_journalmagazine platooning}, month = {March}, pages = {117-131}, title = {A Comparison of Mechanisms for Compensating Negative Impacts of System Integration}, volume = 116, year = 2021 }
%0 Journal Article %1 lesch2020comparison %A Lesch, Veronika %A Krupitzer, Christian %A Stubenrauch, Kevin %A Keil, Nico %A Becker, Christian %A Kounev, Samuel %A Segata, Michele %D 2021 %J Future Generation Computer Systems %P 117-131 %T A Comparison of Mechanisms for Compensating Negative Impacts of System Integration %V 116

A Comparison of Mechanisms for Compensating Negative Impacts of System Integration. Lesch, Veronika; Krupitzer, Christian; Stubenrauch, Kevin; Keil, Nico; Becker, Christian; Kounev, Samuel; Segata, Michele; in Future Generation Computer Systems (2021). 116 117–131.
Special Issue on Self-Aware Computing
[ BibTeX ] [ EndNote ] [ Download ]
 
@article{Kaiser2020Towards, author = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spiess, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel}, journal = {Computers}, keywords = {Internet_of_Things myown Cyber-Physical_Systems Design_of_software_and_systems Analytical_and_simulation-based_analysis Simulation descartes Self-aware-computing t_journalmagazine Intelligent_Transportation_Systems}, month = {Februar}, note = {Special Issue on Self-Aware Computing}, number = 7, publisher = {MDPI}, title = {{Towards Self-Aware Multirotor Formations}}, volume = 9, year = 2020 }
%0 Journal Article %1 Kaiser2020Towards %A Kaiser, Dennis %A Lesch, Veronika %A Rothe, Julian %A Strohmeier, Michael %A Spiess, Florian %A Krupitzer, Christian %A Montenegro, Sergio %A Kounev, Samuel %D 2020 %I MDPI %J Computers %N 7 %T Towards Self-Aware Multirotor Formations %V 9

Artikel in Konferenzband [ nach oben ]

Chamulteon: Coordinated Auto-Scaling of Micro-Services. Bauer, André; Lesch, Veronika; Versluis, Laurens; Ilyushkin, Alexey; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.
@inproceedings{HeGrRyLeKrKo2020-SIMUtools-Network-Online-Adaptation, abstract = {Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.}, author = {Herrnleben, Stefan and Grohmann, Johannes and Rygielski, Pitor and Lesch, Veronika and Krupitzer, Christian and Kounev, Samuel}, booktitle = {Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools)}, keywords = {myown t_full descartes networking}, month = {August}, series = {SIMUtools 2020}, title = {A Simulation-based Optimization Framework for Online Adaptation of Networks}, year = 2020 }
%0 Conference Paper %1 HeGrRyLeKrKo2020-SIMUtools-Network-Online-Adaptation %A Herrnleben, Stefan %A Grohmann, Johannes %A Rygielski, Pitor %A Lesch, Veronika %A Krupitzer, Christian %A Kounev, Samuel %B Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) %D 2020 %T A Simulation-based Optimization Framework for Online Adaptation of Networks %X Today's data centers face a rapid change of deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can be used for optimizing data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering. In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that performance requirements and SLAs are continuously met. We evaluate our framework w.r.t. (i) general requirements and assessments of languages and frameworks for adaptation models, (ii) finding Pareto-optimal solutions considering a multi-dimensional cost model, and (iii) scalability. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.

A Simulation-based Optimization Framework for Online Adaptation of Networks. Herrnleben, Stefan; Grohmann, Johannes; Rygielski, Pitor; Lesch, Veronika; Krupitzer, Christian; Kounev, Samuel; in Proceedings of the 12th EAI International Conference on Simulation Tools and Techniques (SIMUtools) (2020).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{KrLePfBeSe2019_TOPCars, author = {Krupitzer, Christian and Lesch, Veronika and Pfannem{ü}ller, Martin and Becker, Christian and Segata, Michele}, booktitle = {Proceedings of the 1st {ACM} Workshop on Technologies, mOdels, and Protocols for Cooperative Connected Cars (TOP-Cars), Colocated with ACM MobiHoc 2019}, keywords = {Tool Internet_of_Things myown Cyber-Physical_Systems Analytical_and_simulation-based_analysis Simulation descartes t_workshop Intelligent_Transportation_Systems}, month = {July}, publisher = {ACM}, title = {{A Modular Simulation Framework for Analyzing Platooning Coordination}}, year = 2019 }
%0 Conference Paper %1 KrLePfBeSe2019_TOPCars %A Krupitzer, Christian %A Lesch, Veronika %A Pfannemüller, Martin %A Becker, Christian %A Segata, Michele %B Proceedings of the 1st ACM Workshop on Technologies, mOdels, and Protocols for Cooperative Connected Cars (TOP-Cars), Colocated with ACM MobiHoc 2019 %D 2019 %I ACM %T A Modular Simulation Framework for Analyzing Platooning Coordination

Performance Oriented Dynamic Bypassing for Intrusion Detection Systems. Iffländer, Lukas; Stoll, Jonathan; Rawtani, Nishant; Lesch, Veronika; Lange, Klaus-Dieter; Kounev, Samuel; in Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering (2019). 159–166.
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{ZuBaLeKrHeKoCu2019-ICAC-Autonomic, author = {Z{ü}fle, Marwin and Bauer, Andr{é} and Lesch, Veronika and Krupitzer, Christian and Herbst, Nikolas and Kounev, Samuel and Curtef, Valentin}, booktitle = {Proceedings of the 16th {IEEE} International Conference on Autonomic Computing (ICAC)}, keywords = {Statistical_estimation_and_machine_learning Internet_of_Things myown Performance Online_monitoring_and_forecasting t_full descartes Prediction PRISMA}, month = {June}, publisher = {IEEE}, title = {{Autonomic Forecasting Method Selection: Examination and Ways Ahead}}, year = 2019 }
%0 Conference Paper %1 ZuBaLeKrHeKoCu2019-ICAC-Autonomic %A Züfle, Marwin %A Bauer, André %A Lesch, Veronika %A Krupitzer, Christian %A Herbst, Nikolas %A Kounev, Samuel %A Curtef, Valentin %B Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) %D 2019 %I IEEE %T Autonomic Forecasting Method Selection: Examination and Ways Ahead

A Modular Simulation Framework for Analyzing Platooning Coordination. Krupitzer, Christian; Lesch, Veronika; Pfannemüller, Martin; Becker, Christian; Segata, Michele; in Proceedings of the 1st ACM Workshop on Technologies, mOdels, and Protocols for Cooperative Connected Cars (TOP-Cars), Colocated with ACM MobiHoc 2019 (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{BaLeVeIlHeKo2019-ICDCS-Chamulteon, author = {Bauer, Andr{é} and Lesch, Veronika and Versluis, Laurens and Ilyushkin, Alexey and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS)}, keywords = {myown Metrics_and_benchmarking_methodologies Resource_management Online_monitoring_and_forecasting t_full descartes Self-aware-computing Cloud Elasticity Chameleon SPEC}, month = {July}, title = {Chamulteon: Coordinated Auto-Scaling of Micro-Services}, year = 2019 }
%0 Conference Paper %1 BaLeVeIlHeKo2019-ICDCS-Chamulteon %A Bauer, André %A Lesch, Veronika %A Versluis, Laurens %A Ilyushkin, Alexey %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) %D 2019 %T Chamulteon: Coordinated Auto-Scaling of Micro-Services

FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds. Lesch, Veronika; Bauer, André; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018) (2018).
[ Abstract ] [ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
Attacks on software systems are becoming more and more frequent, aggressive and sophisticated. With the changing threat landscape, in 2018, organizations are looking at when they will be attacked, not if. Intrusion Detection Systems (IDSs) can help in defending against these attacks. The systems that host IDSs require extensive computing resources as IDSs tend to detect attacks under overloaded conditions wrongfully. With the end of Moore's law and the growing adoption of Internet of Things, designers of security systems can no longer expect processing power to keep up the pace with them. This limitation requires ways to increase the performance of these systems without adding additional compute power. In this work, we present two dynamic and a static approach to bypass IDS for traffic deemed benign. We provide its prototype implementation and evaluate our solution. Our evaluation shows promising results. Performance is increased up to the level of a system without an IDS. Attack detection is within the margin of error from the 100% rate. However, our findings show that dynamic approaches perform best when using software switches. The use of a hardware switch reduces the detection rate and performance significantly.
@inproceedings{Ifflaender2019_PerformanceOrientedDynamicBypassingforIntrusionDetectionSystems, abstract = {Attacks on software systems are becoming more and more frequent, aggressive and sophisticated. With the changing threat landscape, in 2018, organizations are looking at when they will be attacked, not if. Intrusion Detection Systems (IDSs) can help in defending against these attacks. The systems that host IDSs require extensive computing resources as IDSs tend to detect attacks under overloaded conditions wrongfully. With the end of Moore's law and the growing adoption of Internet of Things, designers of security systems can no longer expect processing power to keep up the pace with them. This limitation requires ways to increase the performance of these systems without adding additional compute power. In this work, we present two dynamic and a static approach to bypass IDS for traffic deemed benign. We provide its prototype implementation and evaluate our solution. Our evaluation shows promising results. Performance is increased up to the level of a system without an IDS. Attack detection is within the margin of error from the 100% rate. However, our findings show that dynamic approaches perform best when using software switches. The use of a hardware switch reduces the detection rate and performance significantly.}, address = {New York, NY, USA}, author = {Iffländer, Lukas and Stoll, Jonathan and Rawtani, Nishant and Lesch, Veronika and Lange, Klaus-Dieter and Kounev, Samuel}, booktitle = {Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering}, keywords = {myown Metrics_and_benchmarking_methodologies Resource_management Networking Security Performance t_short descartes Self-aware-computing EvIDencE Self-adaptive-systems Optimization}, pages = {159&ndash;166}, publisher = {ACM}, series = {ICPE '19}, title = {Performance Oriented Dynamic Bypassing for Intrusion Detection Systems}, year = 2019 }
%0 Conference Paper %1 Ifflaender2019_PerformanceOrientedDynamicBypassingforIntrusionDetectionSystems %A Iffländer, Lukas %A Stoll, Jonathan %A Rawtani, Nishant %A Lesch, Veronika %A Lange, Klaus-Dieter %A Kounev, Samuel %B Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2019 %I ACM %P 159--166 %T Performance Oriented Dynamic Bypassing for Intrusion Detection Systems %U http://doi.acm.org/10.1145/3297663.3310313 %X Attacks on software systems are becoming more and more frequent, aggressive and sophisticated. With the changing threat landscape, in 2018, organizations are looking at when they will be attacked, not if. Intrusion Detection Systems (IDSs) can help in defending against these attacks. The systems that host IDSs require extensive computing resources as IDSs tend to detect attacks under overloaded conditions wrongfully. With the end of Moore's law and the growing adoption of Internet of Things, designers of security systems can no longer expect processing power to keep up the pace with them. This limitation requires ways to increase the performance of these systems without adding additional compute power. In this work, we present two dynamic and a static approach to bypass IDS for traffic deemed benign. We provide its prototype implementation and evaluate our solution. Our evaluation shows promising results. Performance is increased up to the level of a system without an IDS. Attack detection is within the margin of error from the 100% rate. However, our findings show that dynamic approaches perform best when using software switches. The use of a hardware switch reduces the detection rate and performance significantly.

Emerging Self-Integration through Coordination of Autonomous Adaptive Systems. Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; in Proceedings of the 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019 (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{LeKrTo2019_SISSY, author = {Lesch, Veronika and Krupitzer, Christian and Tomforde, Sven}, booktitle = {Proceedings of the 4th {IEEE} International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019}, keywords = {Internet_of_Things myown Cyber-Physical_Systems descartes Self-aware-computing Self-adaptive-systems t_workshop}, month = {June}, publisher = {IEEE}, title = {{Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}}, year = 2019 }
%0 Conference Paper %1 LeKrTo2019_SISSY %A Lesch, Veronika %A Krupitzer, Christian %A Tomforde, Sven %B Proceedings of the 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W@ICAC/SASO 2019 %D 2019 %I IEEE %T Emerging Self-Integration through Coordination of Autonomous Adaptive Systems

Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems. Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; in Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019 (2019).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{LeKrTo2019, author = {Lesch, Veronika and Krupitzer, Christian and Tomforde, Sven}, booktitle = {Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019}, keywords = {Internet_of_Things myown descartes Multi-criteria_optimization Self-adaptive-systems t_workshop Intelligent_Transportation_Systems Optimization}, month = {may}, publisher = {Gesellschaft fuer Informatik (GI)}, title = {{Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems}}, year = 2019 }
%0 Conference Paper %1 LeKrTo2019 %A Lesch, Veronika %A Krupitzer, Christian %A Tomforde, Sven %B Proceedings of the 7th edition in the Series on Autonomously Learning and Optimising Systems (SAOS), co-located with 32nd GI/ITG ARCS 2019 %D 2019 %I Gesellschaft fuer Informatik (GI) %T Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems

Autonomic Forecasting Method Selection: Examination and Ways Ahead. Züfle, Marwin; Bauer, André; Lesch, Veronika; Krupitzer, Christian; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; in Proceedings of the 16th IEEE International Conference on Autonomic Computing (ICAC) (2019).
Full Paper Acceptance Ratio: 23,7%
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@inproceedings{LeBaHeKo-ICPE18-FOX-AS-COST, address = {New York, NY, USA}, author = {Lesch, Veronika and Bauer, Andr{é} and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018)}, keywords = {myown Resource_management Performance t_full descartes Prediction Elasticity Optimization Tool Online_monitoring_and_forecasting Self-aware-computing Cloud BUNGEE}, month = {April}, note = {Full Paper Acceptance Ratio: 23,7%}, publisher = {ACM}, title = {{FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds}}, year = 2018 }
%0 Conference Paper %1 LeBaHeKo-ICPE18-FOX-AS-COST %A Lesch, Veronika %A Bauer, André %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018) %C New York, NY, USA %D 2018 %I ACM %T FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds %U https://doi.org/10.1145/3184407.3184415

Kapitel (Teil eines Buches) [ nach oben ]

Toward a Framework for Self-Learning Adaptation Planning through Optimization. Lesch, Veronika; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).
To appear
[ BibTeX ] [ EndNote ]
 
@incollection{lesch2020toward, author = {Lesch, Veronika}, booktitle = {Organic Computing: Doctoral Dissertation Colloquium 2020}, keywords = {myown industry_4_0 self-aware-computing optimization t_full descartes intelligent_transportation_systems cyber-physical_systems}, note = {To appear}, title = {Toward a Framework for Self-Learning Adaptation Planning through Optimization}, year = 2020 }
%0 Book Section %1 lesch2020toward %A Lesch, Veronika %B Organic Computing: Doctoral Dissertation Colloquium 2020 %D 2020 %T Toward a Framework for Self-Learning Adaptation Planning through Optimization

Report [ nach oben ]

A Survey on Human Machine Interaction in Industry 4.0 Krupitzer, Christian; Müller, Sebastian; Lesch, Veronika; Züfle, Marwin; Edinger, Janick; Lemken, Alexander; Schäfer, Dominik; Kounev, Samuel; Becker, Christian; (2020).
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@techreport{Krupitzer2020ASurveyPdM, author = {Krupitzer, Christian and Wagenhals, Tim and Z{ü}fle, Marwin and Lesch, Veronika and Sch{ä}fer, Dominik and Mozaffarin, Amin and Edinger, Janick and Becker, Christian and Kounev, Samuel}, institution = {Universit{ä}t W{ü}rzburg and University of Mannheim and Syntax Systems GmbH and MOZYS Engineering GmbH}, journal = {arXiv}, keywords = {Internet_of_Things Survey myown Cyber-Physical_Systems Online_monitoring_and_forecasting descartes Prediction Industry_4_0 t_techreport Reliability}, month = {feb}, title = {{A Survey on Predictive Maintenance for Industry 4.0}}, year = 2020 }
%0 Report %1 Krupitzer2020ASurveyPdM %A Krupitzer, Christian %A Wagenhals, Tim %A Züfle, Marwin %A Lesch, Veronika %A Schäfer, Dominik %A Mozaffarin, Amin %A Edinger, Janick %A Becker, Christian %A Kounev, Samuel %D 2020 %J arXiv %T A Survey on Predictive Maintenance for Industry 4.0 %U https://arxiv.org/abs/2002.08224

A Survey on Predictive Maintenance for Industry 4.0 Krupitzer, Christian; Wagenhals, Tim; Züfle, Marwin; Lesch, Veronika; Schäfer, Dominik; Mozaffarin, Amin; Edinger, Janick; Becker, Christian; Kounev, Samuel; (2020).
[ BibTeX ] [ EndNote ] [ URL ] [ Download ]
 
@techreport{Krupitzer2020ASurvey, author = {Krupitzer, Christian and M{ü}ller, Sebastian and Lesch, Veronika and Z{ü}fle, Marwin and Edinger, Janick and Lemken, Alexander and Sch{ä}fer, Dominik and Kounev, Samuel and Becker, Christian}, institution = {Universit{ä}t W{ü}rzburg and University of Mannheim and ioxp GmbH and Syntax Systems GmbH}, journal = {arXiv}, keywords = {Survey myown descartes Industry_4_0 t_techreport}, month = {feb}, title = {{A Survey on Human Machine Interaction in Industry 4.0}}, year = 2020 }
%0 Report %1 Krupitzer2020ASurvey %A Krupitzer, Christian %A Müller, Sebastian %A Lesch, Veronika %A Züfle, Marwin %A Edinger, Janick %A Lemken, Alexander %A Schäfer, Dominik %A Kounev, Samuel %A Becker, Christian %D 2020 %J arXiv %T A Survey on Human Machine Interaction in Industry 4.0 %U https://arxiv.org/abs/2002.01025

Sonstiges [ nach oben ]

Towards Self-Aware Industry 4.0 using a Layered Meta-Model. Lesch, Veronika; (2018, April).
[ BibTeX ] [ EndNote ] [ Download ]
 
@misc{talk-Lesch-SelPhyS19, author = {Lesch, Veronika and Krupitzer, Christian and Tomforde, Sven}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2019}, keywords = {Internet_of_Things myown Cyber-Physical_Systems se2_talk t_talk descartes Multi-criteria_optimization Talks Self-adaptive-systems Optimization}, month = {April}, title = {{Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems}}, year = 2019 }
%0 Generic %1 talk-Lesch-SelPhyS19 %A Lesch, Veronika %A Krupitzer, Christian %A Tomforde, Sven %D 2019 %T Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems

Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems. Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; (2019, April).
[ BibTeX ] [ EndNote ] [ Download ]
 
@misc{talk-Lesch-SelPhyS, author = {Lesch, Veronika}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2018}, keywords = {myown Performance se2_talk t_talk descartes Meta-models Talks Self-adaptive-systems Optimization}, month = {April}, title = {{Towards Self-Aware Industry 4.0 using a Layered Meta-Model}}, year = 2018 }
%0 Generic %1 talk-Lesch-SelPhyS %A Lesch, Veronika %D 2018 %T Towards Self-Aware Industry 4.0 using a Layered Meta-Model