Publications

Journal and Magazine Articles

Self-Aware Optimization of Adaptation Planning Strategies Lesch, Veronika; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; in ACM Transactions on Autonomous and Adaptive Systems (2022).
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In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.

@article{lesch2022selfaware, abstract = {In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.}, author = {Lesch, Veronika and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {myown}, month = {oct}, title = {Self-Aware Optimization of Adaptation Planning Strategies}, year = 2022 }
A literature review on optimization techniques for adaptation planning in adaptive systems: State of the art and research directions Henrichs, Elia; Lesch, Veronika; Straesser, Martin; Kounev, Samuel; Krupitzer, Christian; in Information and Software Technology (2022). 149 106940.
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Context: Recent developments in modern IT systems including internet of things, edge/fog computing, or cyber–physical systems support intelligent and seamless interaction between users and systems. This requires a reaction to changes in their environment or the system. Adaptive systems provide mechanisms for these reactions. Objective: To implement this functionality, several approaches for the planning of adaptations exist that rely on rules, utility functions, or advanced techniques, such as machine learning. As the adaptation space with possible options is often extensively huge, optimization techniques might support efficient determination of the adaptation space and identify the system’s optimal configuration. With this paper, we provide a systematic review of adaptation planning as the optimization target. Method: In this paper, we review which optimization techniques are applied for adaptation planning in adaptive systems using a systematic literature review approach. Results: We reviewed 115 paper in detail out of an initial search set of 9,588 papers. Our analysis reveals that learning techniques and genetic algorithms are by far dominant; in total, heuristics (anytime learning) are more frequently applied as exact algorithms. We observed that around 57% of the approaches target multi-objectiveness and around 30% integrate distributed optimization. As last dimension, we focused on situation-awareness, which is only supported by two approaches. Conclusion: In this paper, we provide an overview of the current state of the art of approaches that rely on optimization techniques for planning adaptations in adaptive systems and further derive open research challenges, in particular regarding the integration of distributed optimization and situation-awareness.

@article{HENRICHS2022106940, abstract = {Context: Recent developments in modern IT systems including internet of things, edge/fog computing, or cyber–physical systems support intelligent and seamless interaction between users and systems. This requires a reaction to changes in their environment or the system. Adaptive systems provide mechanisms for these reactions. Objective: To implement this functionality, several approaches for the planning of adaptations exist that rely on rules, utility functions, or advanced techniques, such as machine learning. As the adaptation space with possible options is often extensively huge, optimization techniques might support efficient determination of the adaptation space and identify the system’s optimal configuration. With this paper, we provide a systematic review of adaptation planning as the optimization target. Method: In this paper, we review which optimization techniques are applied for adaptation planning in adaptive systems using a systematic literature review approach. Results: We reviewed 115 paper in detail out of an initial search set of 9,588 papers. Our analysis reveals that learning techniques and genetic algorithms are by far dominant; in total, heuristics (anytime learning) are more frequently applied as exact algorithms. We observed that around 57% of the approaches target multi-objectiveness and around 30% integrate distributed optimization. As last dimension, we focused on situation-awareness, which is only supported by two approaches. Conclusion: In this paper, we provide an overview of the current state of the art of approaches that rely on optimization techniques for planning adaptations in adaptive systems and further derive open research challenges, in particular regarding the integration of distributed optimization and situation-awareness.}, author = {Henrichs, Elia and Lesch, Veronika and Straesser, Martin and Kounev, Samuel and Krupitzer, Christian}, journal = {Information and Software Technology}, keywords = {myown}, pages = 106940, title = {A literature review on optimization techniques for adaptation planning in adaptive systems: State of the art and research directions}, volume = 149, year = 2022 }
A Case Study on Optimization of Warehouses Lesch, Veronika; M{\"u}ller, Patrick; Kr{\"a}mer, Moritz; Kounev, Samuel; Krupitzer, Christian; in arXiv preprint arXiv:2112.12058 (2021).
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@article{lesch2021case, author = {Lesch, Veronika and M{\"u}ller, Patrick and Kr{\"a}mer, Moritz and Kounev, Samuel and Krupitzer, Christian}, journal = {arXiv preprint arXiv:2112.12058}, keywords = {myown}, title = {A Case Study on Optimization of Warehouses}, year = 2021 }
Tackling the Rich Vehicle Routing Problem with Nature-Inspired Algorithms Lesch, Veronika; König, Maximilian; Kounev, Samuel; Stein, Anthony; Krupitzer, Christian; in Applied Intelligence (2021). 52 9476–9500.
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@article{lesch2021tackling, author = {Lesch, Veronika and König, Maximilian and Kounev, Samuel and Stein, Anthony and Krupitzer, Christian}, journal = {Applied Intelligence}, keywords = {select}, pages = {9476–9500}, title = {Tackling the Rich Vehicle Routing Problem with Nature-Inspired Algorithms}, volume = 52, year = 2021 }
An Overview on Approaches for Coordination of Platoons Lesch, Veronika; Breitbach, Martin; Segata, Michele; Becker, Christian; Kounev, Samuel; Krupitzer, Christian; in IEEE Transactions on Intelligent Transportation Systems (2021).

Early Access on IEEE Xplore
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@article{lesch2021overview, author = {Lesch, Veronika and Breitbach, Martin and Segata, Michele and Becker, Christian and Kounev, Samuel and Krupitzer, Christian}, journal = {IEEE Transactions on Intelligent Transportation Systems}, keywords = {descartes}, note = {Early Access on IEEE Xplore}, title = {An Overview on Approaches for Coordination of Platoons}, year = 2021 }
A Machine Learning-based Workflow for Automatic Detection of Anomalies in Machine Tools Züfle, Marwin; Moog, Felix; Lesch, Veronika; Krupitzer, Christian; Kounev, Samuel; in ISA Transactions: The Journal of Automation (2021).

(in press)
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Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.

@article{zuefle2021anomalydetection, abstract = {Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.}, author = {Züfle, Marwin and Moog, Felix and Lesch, Veronika and Krupitzer, Christian and Kounev, Samuel}, journal = {ISA Transactions: The Journal of Automation}, keywords = {myown}, note = {(in press)}, title = {A Machine Learning-based Workflow for Automatic Detection of Anomalies in Machine Tools}, year = 2021 }
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.
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@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 = {compensation}, month = {March}, pages = {117-131}, title = {A Comparison of Mechanisms for Compensating Negative Impacts of System Integration}, volume = 116, year = 2021 }
{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) MDPI.

Special Issue on Self-Aware Computing
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@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 = {myown}, month = {Februar}, note = {Special Issue on Self-Aware Computing}, number = 7, publisher = {MDPI}, title = {{Towards Self-Aware Multirotor Formations}}, volume = 9, year = 2020 }

Full Papers

ComBench: A Benchmarking Framework for Publish/Subscribe Communication Protocols Under Network Limitations Herrnleben, Stefan; Leidinger, Maximilian; Lesch, Veronika; Prantl, Thomas; Grohmann, Johannes; Krupitzer, Christian; Kounev, Samuel; in Performance Evaluation Methodologies and Tools, Q. Zhao, L. Xia (eds.) (2021). 72–92. Springer International Publishing, Cham.
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Efficient and dependable communication is a highly relevant aspect for Internet of Things (IoT) systems in which tiny sensors, actuators, wearables, or other smart devices exchange messages. Various publish/subscribe protocols address the challenges of communication in IoT systems. The selection process of a suitable protocol should consider the communication behavior of the application, the protocol's performance, the resource requirements on the end device, and the network connection quality, as IoT environments often rely on wireless networks. Benchmarking is a common approach to evaluate and compare systems, considering the performance and aspects like dependability or security. In this paper, we present our IoT communication benchmarking framework ComBench for publish/subscribe protocols focusing on constrained networks with varying quality conditions. The benchmarking framework supports system designers, software engineers, and application developers to select and investigate the behavior of communication protocols. Our benchmarking framework contributes to (i) show the impact of fluctuating network quality on communication, (ii) compare multiple protocols, protocol features, and protocol implementations, and (iii) analyze scalability, robustness, and dependability of clients, networks, and brokers in different scenarios. Our case study demonstrates the applicability of our framework to support the decision for the best-suited protocol in various scenarios.

@inproceedings{10.1007/978-3-030-92511-6_5, abstract = {Efficient and dependable communication is a highly relevant aspect for Internet of Things (IoT) systems in which tiny sensors, actuators, wearables, or other smart devices exchange messages. Various publish/subscribe protocols address the challenges of communication in IoT systems. The selection process of a suitable protocol should consider the communication behavior of the application, the protocol's performance, the resource requirements on the end device, and the network connection quality, as IoT environments often rely on wireless networks. Benchmarking is a common approach to evaluate and compare systems, considering the performance and aspects like dependability or security. In this paper, we present our IoT communication benchmarking framework ComBench for publish/subscribe protocols focusing on constrained networks with varying quality conditions. The benchmarking framework supports system designers, software engineers, and application developers to select and investigate the behavior of communication protocols. Our benchmarking framework contributes to (i) show the impact of fluctuating network quality on communication, (ii) compare multiple protocols, protocol features, and protocol implementations, and (iii) analyze scalability, robustness, and dependability of clients, networks, and brokers in different scenarios. Our case study demonstrates the applicability of our framework to support the decision for the best-suited protocol in various scenarios.}, address = {Cham}, author = {Herrnleben, Stefan and Leidinger, Maximilian and Lesch, Veronika and Prantl, Thomas and Grohmann, Johannes and Krupitzer, Christian and Kounev, Samuel}, booktitle = {Performance Evaluation Methodologies and Tools}, editor = {Zhao, Qianchuan and Xia, Li}, keywords = {myown}, pages = {72–92}, publisher = {Springer International Publishing}, title = {ComBench: A Benchmarking Framework for Publish/Subscribe Communication Protocols Under Network Limitations}, year = 2021 }
Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies Lesch, Veronika; Noack, Tanja; Hefter, Johannes; Kounev, Samuel; Krupitzer, Christian; in Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2021 (2021). IEEE.

Best Paper Award Runner Up, Full Paper Acceptance Ratio: 25%
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@inproceedings{lesch2021towards, author = {Lesch, Veronika and Noack, Tanja and Hefter, Johannes and Kounev, Samuel and Krupitzer, Christian}, booktitle = {Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2021}, keywords = {myown}, note = {Best Paper Award Runner Up, Full Paper Acceptance Ratio: 25%}, publisher = {IEEE}, title = {Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies}, year = 2021 }
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), H. Song, D. Jiang (eds.) (2021). 513–532. Springer International Publishing, Cham.
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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.}, address = {Cham}, 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)}, editor = {Song, Houbing and Jiang, Dingde}, keywords = {t_full}, month = {August}, pages = {513–532}, publisher = {Springer International Publishing}, series = {SIMUtools 2020}, title = {A Simulation-based Optimization Framework for Online Adaptation of Networks}, year = 2021 }
Toward a Framework for Self-Learning Adaptation Planning through Optimization Lesch, Veronika; in Organic Computing: Doctoral Dissertation Colloquium 2020 (2020).
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@incollection{lesch2020toward, author = {Lesch, Veronika}, booktitle = {Organic Computing: Doctoral Dissertation Colloquium 2020}, keywords = {t_full}, title = {Toward a Framework for Self-Learning Adaptation Planning through Optimization}, year = 2020 }
{Autonomic Forecasting Method Selection: Examination and Ways Ahead} Z{\"u}fle, Marwin; Bauer, Andr{\’e}; Lesch, Veronika; Krupitzer, Christian; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; in Proceedings of the 16th {IEEE} International Conference on Autonomic Computing (ICAC) (2019). IEEE.
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@inproceedings{ZuBaLeKrHeKoCu2019-ICAC-Autonomic, author = {Z{\"u}fle, Marwin and Bauer, Andr{\'e} 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 = {myown}, month = {June}, publisher = {IEEE}, title = {{Autonomic Forecasting Method Selection: Examination and Ways Ahead}}, year = 2019 }
Chamulteon: Coordinated Auto-Scaling of Micro-Services Bauer, Andr{\’e}; Lesch, Veronika; Versluis, Laurens; Ilyushkin, Alexey; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019).
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@inproceedings{BaLeVeIlHeKo2019-ICDCS-Chamulteon, author = {Bauer, Andr{\'e} 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}, month = {July}, title = {Chamulteon: Coordinated Auto-Scaling of Micro-Services}, year = 2019 }
{FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds} Lesch, Veronika; Bauer, Andr{\’e}; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018) (2018). ACM, New York, NY, USA.

Full Paper Acceptance Ratio: 23,7%
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@inproceedings{LeBaHeKo-ICPE18-FOX-AS-COST, address = {New York, NY, USA}, author = {Lesch, Veronika and Bauer, Andr{\'e} and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018)}, keywords = {Prediction}, month = {April}, note = {Full Paper Acceptance Ratio: 23,7%}, publisher = {ACM}, title = {{FOX: Cost-Awareness for Autonomic Resource Management in Public Clouds}}, year = 2018 }

Short Papers

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. ACM, New York, NY, USA.
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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 = {t_short}, pages = {159–166}, publisher = {ACM}, series = {ICPE '19}, title = {Performance Oriented Dynamic Bypassing for Intrusion Detection Systems}, year = 2019 }

Workshop Papers

Utility-based Vehicle Routing Integrating User Preferences Lesch, Veronika; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; in Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021 (2021). IEEE.
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@inproceedings{lesch2021utility, author = {Lesch, Veronika and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, booktitle = {Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021}, keywords = {myown}, month = {March}, publisher = {IEEE}, title = {Utility-based Vehicle Routing Integrating User Preferences}, year = 2021 }
{A Modular Simulation Framework for Analyzing Platooning Coordination} Krupitzer, Christian; Lesch, Veronika; Pfannem{\"u}ller, 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). ACM.
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@inproceedings{KrLePfBeSe2019_TOPCars, author = {Krupitzer, Christian and Lesch, Veronika and Pfannem{\"u}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 = {myown}, month = {July}, publisher = {ACM}, title = {{A Modular Simulation Framework for Analyzing Platooning Coordination}}, year = 2019 }
{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). IEEE.
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@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 = {myown}, month = {June}, publisher = {IEEE}, title = {{Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}}, year = 2019 }
{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). Gesellschaft fuer Informatik (GI).
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@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 = {myown}, month = {may}, publisher = {Gesellschaft fuer Informatik (GI)}, title = {{Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems}}, year = 2019 }

Vision and Position Papers

FADE: Towards Flexible and Adaptive Distance Estimation Considering Obstacles Hadry, Marius; Lesch, Veronika; Kounev, Samuel; in Companion of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE 2022) (2022). ACM.
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@inproceedings{hadry2022towards, author = {Hadry, Marius and Lesch, Veronika and Kounev, Samuel}, booktitle = {Companion of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE 2022)}, keywords = {myown}, month = {April}, publisher = {ACM}, title = {FADE: Towards Flexible and Adaptive Distance Estimation Considering Obstacles}, year = 2022 }

Talks

{Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems} Lesch, Veronika; Krupitzer, Christian; Tomforde, Sven; (2019, April).
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@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 = {myown}, month = {April}, title = {{Please Obey My Plan: How to Optimise Decentralised Self-Adaptive Systems}}, year = 2019 }
{Towards Self-Aware Industry 4.0 using a Layered Meta-Model} Lesch, Veronika; (2018, April).
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@misc{talk-Lesch-SelPhyS, author = {Lesch, Veronika}, howpublished = {Talk at Workshop on Self-Awareness in Cyber-Physical Systems 2018}, keywords = {myown}, month = {April}, title = {{Towards Self-Aware Industry 4.0 using a Layered Meta-Model}}, year = 2018 }

Techreports

A Case Study on Optimization of Warehouses Lesch, Veronika; Müller, Patrick B.M.; Krämer, Moritz; Kounev, Samuel; Krupitzer, Christian; (2021).
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@techreport{lesch2021study, author = {Lesch, Veronika and Müller, Patrick B.M. and Krämer, Moritz and Kounev, Samuel and Krupitzer, Christian}, keywords = {myown}, title = {A Case Study on Optimization of Warehouses}, year = 2021 }
A Case Study of Vehicle Route Optimization Lesch, Veronika; König, Maximilian; Kounev, Samuel; Stein, Anthony; Krupitzer, Christian; (2021). Universit{ä}t W{ü}rzburg and PASS Logistics Solutions AG and Universit{ä}t Hohenheim.
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@techreport{lesch2021studyvrp, author = {Lesch, Veronika and König, Maximilian and Kounev, Samuel and Stein, Anthony and Krupitzer, Christian}, institution = {Universit{ä}t W{ü}rzburg and PASS Logistics Solutions AG and Universit{ä}t Hohenheim}, keywords = {myown}, title = {A Case Study of Vehicle Route Optimization}, year = 2021 }
A Case Study on Optimization of Platooning Coordination Lesch, Veronika; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; (2021). Universit{ä}t W{ü}rzburg and Universit{ä}t Hohenheim.
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@techreport{lesch2021studyplatooning, author = {Lesch, Veronika and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, institution = {Universit{ä}t W{ü}rzburg and Universit{ä}t Hohenheim}, keywords = {myown}, title = {A Case Study on Optimization of Platooning Coordination}, year = 2021 }
{A Survey on Predictive Maintenance for Industry 4.0} Krupitzer, Christian; Wagenhals, Tim; Z{\"u}fle, Marwin; Lesch, Veronika; Sch{\"a}fer, Dominik; Mozaffarin, Amin; Edinger, Janick; Becker, Christian; Kounev, Samuel; (2020). Universit{\"a}t W{\"u}rzburg and University of Mannheim and Syntax Systems GmbH and MOZYS Engineering GmbH.
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@techreport{Krupitzer2020ASurveyPdM, author = {Krupitzer, Christian and Wagenhals, Tim and Z{\"u}fle, Marwin and Lesch, Veronika and Sch{\"a}fer, Dominik and Mozaffarin, Amin and Edinger, Janick and Becker, Christian and Kounev, Samuel}, institution = {Universit{\"a}t W{\"u}rzburg and University of Mannheim and Syntax Systems GmbH and MOZYS Engineering GmbH}, journal = {arXiv}, keywords = {myown}, month = {feb}, title = {{A Survey on Predictive Maintenance for Industry 4.0}}, year = 2020 }
{A Survey on Human Machine Interaction in Industry 4.0} Krupitzer, Christian; M{\"u}ller, Sebastian; Lesch, Veronika; Z{\"u}fle, Marwin; Edinger, Janick; Lemken, Alexander; Sch{\"a}fer, Dominik; Kounev, Samuel; Becker, Christian; (2020). Universit{\"a}t W{\"u}rzburg and University of Mannheim and ioxp GmbH and Syntax Systems GmbH.
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@techreport{Krupitzer2020ASurvey, author = {Krupitzer, Christian and M{\"u}ller, Sebastian and Lesch, Veronika and Z{\"u}fle, Marwin and Edinger, Janick and Lemken, Alexander and Sch{\"a}fer, Dominik and Kounev, Samuel and Becker, Christian}, institution = {Universit{\"a}t W{\"u}rzburg and University of Mannheim and ioxp GmbH and Syntax Systems GmbH}, journal = {arXiv}, keywords = {myown}, month = {feb}, title = {{A Survey on Human Machine Interaction in Industry 4.0}}, year = 2020 }

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