Publications

My research is often located at the intersection of distributed systems, operating systems, and database systems, yet I am generally passionate about computer systems research. I have worked on distributed computer systems, edge and cloud computing, data-intensive systems, resource management and scheduling, carbon-aware computing, as well as software development and operation tools.

papers:
  • Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling. Jonathan Will, Nico Treide, Lauritz Thamsen, and Odej Kao. To appear in the Proceedings of the 2024 IEEE International Conference on Big Data (Big Data). To be presented at the 7th International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD). IEEE. 2024. Workshop Paper
  • Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows. Rafael Ferreira da Silva, Deborah Bard, Kyle Chard, ..., Lauritz Thamsen, ..., Qi Yu, Ziad Al Bkhetan, Mahnoor Zulfiqar. Technical Report, ORNL/TM−2024/3573. Oak Ridge National Laboratory. 2024. Technical Report
  • KS+: Predicting Workflow Task Memory Usage Over Time. Jonathan Bader, Ansgar Lößer, Lauritz Thamsen, Björn Scheuermann, and Odej Kao. To appear in the Proceedings of the 20th IEEE International Conference on e-Science (e-Science). To be presented at the 4th Workshop on Reproducible Workflows, Data Management, and Security (ReWorDS). IEEE. 2024. Workshop Paper [pdf] [code]
    • To be published by IEEE. © IEEE 2024. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Ponder: Online Prediction of Task Memory Requirements for Scientific Workflows. Fabian Lehmann, Jonathan Bader, Ninon De Mecquenem, Xing Wang, Vasilis Bountris, Florian Friederici, Ulf Leser, and Lauritz Thamsen. To appear in the Proceedings of the 20th IEEE International Conference on e-Science (e-Science). IEEE. 2024. Conference Paper [pdf] [code] [data]
    • To be published by IEEE. © IEEE 2024. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Sizey: Memory-Efficient Execution of Scientific Workflow Tasks. Jonathan Bader, Fabian Skalski, Fabian Lehmann, Dominik Scheinert, Jonathan Will, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 26th IEEE International Conference on Cluster Computing (Cluster). IEEE. 2024. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/CLUSTER59578.2024.00039, © IEEE 2024. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization. Morgan Geldenhuys, Dominik Scheinert, Odej Kao, and Lauritz Thamsen. In the Proceedings of the 15th ACM/SPEC International Conference on Performance Engineering (ICPE). ACM. 2024. Conference Paper [pdf] [code]
  • Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling. Jonathan Will, Dominik Scheinert, Seraphin Zunzer, Jan Bode, Cedric Kring, and Lauritz Thamsen. In the Companion of the 15th ACM/SPEC International Conference on Performance Engineering (ICPE). Presented at the 9th Workshop on Challenges in Performance Methods for Software Development (WOSP-C). ACM. 2024. Workshop Paper [pdf] [code]
  • Coupled Simulation of Urban Water Networks and Interconnected Critical Urban Infrastructure Systems: A Systematic Review and Multi-Sector Research Agenda. Siling Chen, Florian Brokhausen, Philipp Wiesner, Dóra Hegyi, Muzaffer Citir, Margaux Huth, Sangyoung Park, Jochen Rabe, Lauritz Thamsen, Franz Tscheikner-Gratl, Andrea Castelletti, Paul Uwe Thamsen, and Andrea Cominola. In Sustainable Cities and Society 104. Elsevier. 2024. Journal Paper [pdf]
  • FedZero: Leveraging Renewable Excess Energy in Federated Learning. Philipp Wiesner, Ramin Khalili, Dennis Grinwald, Pratik Agrawal, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy). ACM. 2024. Conference Paper [pdf] [code]
  • Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures. Jonathan Bader, Fabian Lehmann, Lauritz Thamsen, Ulf Leser, and Odej Kao. In Future Generation Computer Systems 150. Elsevier. 2024. Journal Paper [pdf] [code] [data]
  • Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications. Dominik Scheinert, Soeren Becker, Jonathan Will, Luis Englaender, and Lauritz Thamsen. In the Proceedings of the 2023 IEEE International Conference on Big Data (Big Data). Presented at the 11th International Workshop on Distributed Storage and Blockchain Technologies for Big Data. IEEE. 2023. Workshop Paper [pdf] [code]
    • https://doi.org/10.1109/BigData59044.2023.10386195, © IEEE 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Predicting Dynamic Memory Requirements for Scientific Workflow Tasks. Jonathan Bader, Nils Diedrich, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 2023 IEEE International Conference on Big Data (Big Data). IEEE. 2023. Short Conference Paper [pdf] [code] [data]
    • https://doi.org/10.1109/BigData59044.2023.10386837D, © IEEE 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • The Common Workflow Scheduler Interface: Status Quo and Future Plans. Fabian Lehmann, Jonathan Bader, Lauritz Thamsen, and Ulf Leser. In the Workshop Proceedings of the SC'23 International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W). Presented at the 18th Workshop on Workflows in Support of Large-Scale Science (WORKS). ACM. 2023. Workshop Paper [pdf]
    • This work was published as part of the paper on Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments, https://doi.org/10.1145/3624062.3626283 © ACM, 2023. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive Version of Record was published in the SC-W'23 proceedings.
  • Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics. Dominik Scheinert, Philipp Wiesner, Thorsten Wittkopp, Lauritz Thamsen, Jonathan Will, and Odej Kao. In the Proceedings of the 42nd IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2023. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/IPCCC59175.2023.10253884, © IEEE 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings. Demetris Trihinas and Lauritz Thamsen. In the Proceedings of the Euro-Par 2023 Workshops (Euro-Par). Presented in the poster session of the conference. Springer. 2023. Poster Paper [pdf]
  • Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?. Jonathan Will, Lauritz Thamsen, Dominik Scheinert, and Odej Kao. In the Proceedings of the 35th International Conference on Scientific and Statistical Database Management (SSDBM). ACM. 2023. Short Conference Paper [pdf]
    • © ACM, 2023. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the SSDBM'23 Proceedings.
  • Towards a Real-Time IoT: Approaches for Incoming Packet Processing in Cyber-Physical Systems. Ilja Behnke, Christoph Blumschein, Robert Danicki, Philipp Wiesner, Lauritz Thamsen, and Odej Kao. In the Journal of Systems Architecture 140. Elsevier. 2023. Journal Paper [pdf]
  • How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface. Fabian Lehmann, Jonathan Bader, Friedrich Tschirpke, Lauritz Thamsen, and Ulf Leser. In the Proceedings of the 23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE. 2023. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/CCGrid57682.2023.00025, © IEEE 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments. Dominik Scheinert, Babak Sistani Zadeh Aghdam, Soeren Becker, Odej Kao, and Lauritz Thamsen. In the Proceedings of the 2022 IEEE International Conference on Big Data (Big Data). Presented at the Fifth International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2022. Workshop Paper [pdf]
    • https://doi.org/10.1109/BigData55660.2022.10021129, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing. Jonathan Will, Lauritz Thamsen, Jonathan Bader, Dominik Scheinert, and Odej Kao. In the Proceedings of the 2022 IEEE International Conference on Big Data (Big Data). IEEE. 2022. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/BigData55660.2022.10020295, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics. Dominik Scheinert, Soeren Becker, Jonathan Bader, Lauritz Thamsen, Jonathan Will, and Odej Kao. In the Proceedings of the 2022 IEEE International Conference on Big Data (Big Data). IEEE. 2022. Short Conference Paper [pdf] [code]
    • https://doi.org/10.1109/BigData55660.2022.10020860, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures. Jonathan Bader, Fabian Lehmann, Alexander Groth, Lauritz Thamsen, Dominik Scheinert, Jonathan Will, Ulf Leser, and Odej Kao. In the Proceedings of the 41st IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2022. Short Conference Paper [pdf] [code]
    • https://doi.org/10.1109/IPCCC55026.2022.9894299, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services. Demetris Trihinas, Lauritz Thamsen, Jossekin Beilharz, and Moysis Symeonides. In the Proceedings of the 10th IEEE International Conference on Cloud Engineering (IC2E). Presented at the Second International Workshop on Testing Distributed Internet of Things Systems (TDIS). IEEE. 2022. Workshop Paper [pdf]
    • https://doi.org/10.1109/IC2E55432.2022.00011, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing. Morgan Geldenhuys, Ben Pfister, Dominik Scheinert, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 17th Conference on Computer Science and Information Systems (FedCSIS). Presented in the 12th Workshop on Scalable Computing (WSC). IEEE. 2022. Workshop Paper [pdf] [code]
    • https://doi.org/10.15439/2022F225, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning. Houkun Zhu, Dominik Scheinert, Lauritz Thamsen, Kordian Gontarska, and Odej Kao. In the Proceedings of the 10th IEEE International Conference on Cloud Engineering (IC2E). IEEE. 2022. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/IC2E55432.2022.00023, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing. Jonathan Will, Lauritz Thamsen, Jonathan Bader, Dominik Scheinert, and Odej Kao. In the Proceedings of the 10th IEEE International Conference on Cloud Engineering (IC2E). IEEE. 2022. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/IC2E55432.2022.00014, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters. Jonathan Bader, Fabian Lehmann, Lauritz Thamsen, Jonathan Will, Ulf Leser, and Odej Kao. In the Proceedings of the 34th International Conference on Scientific and Statistical Database Management (SSDBM). ACM. 2022. Conference Paper [pdf] [code]
    • © ACM, 2022. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the SSDBM'22 Proceedings.
  • Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads. Morgan Geldenhuys, Dominik Scheinert, Odej Kao, and Lauritz Thamsen. In the Proceedings of the 20th IEEE International Conference on Web Services (ICWS). IEEE. 2022. Best Paper Award Conference Paper [pdf] [code]
    • https://doi.org/10.1109/ICWS55610.2022.00041, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Collaborative Cluster Configuration for Distributed Data-Parallel Processing: A Research Overview. Lauritz Thamsen, Dominik Scheinert, Jonathan Will, Jonathan Bader, and Odej Kao. In Datenbank-Spektrum 22. Springer. 2022. Journal Paper [pdf]
  • Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads. Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 28th International European Conference on Parallel and Distributed Computing (Euro-Par). Springer. 2022. Results featured in a BIFOLD news article. Conference Paper [pdf] [code]
  • Differentiating Network Flows for Priority-Aware Scheduling of Incoming Packets in Real-Time IoT Systems. Christoph Blumschein, Ilja Behnke, Lauritz Thamsen, and Odej Kao. In the Proceedings of the IEEE 25th International Symposium on Real-Time Distributed Computing (ISORC). IEEE. 2022. Conference Paper [pdf]
    • https://doi.org/10.1109/ISORC52572.2022.9812841, © IEEE 2022. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed Edge Networks. Daniel Habenich, Kevin Kreutz, Sören Becker, Jonathan Bader, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking (EdgeSys), co-located with the 17th European Conference on Computer Systems (EuroSys). ACM. 2022. Workshop Paper [pdf] [code]
    • © ACM, 2022. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the EdgeSys'22 Proceedings.
  • The Methods of Cloud Computing. Lauritz Thamsen, Jossekin Beilharz, Andreas Polze, and Odej Kao. Technical Report. Technische Universität Berlin. 2022. Technical Report [pdf]
  • A Priority-Aware Multiqueue NIC Design for Real-Time IoT Devices. Ilja Behnke, Philipp Wiesner, Robert Danicki, and Lauritz Thamsen. In the Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (SAC). Presented as a poster in the Embedded Systems Track (EMBS). ACM. 2022. Poster Paper [pdf] [video]
    • © ACM, 2022. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published with the SAC'22 Proceedings.
  • Continuously Testing Distributed IoT Systems: An Overview of the State of the Art. Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer, Dirk Friedenberger, Florian Brokhausen, Lukas Pirl, Ilja Behnke, Andreas Polze, and Lauritz Thamsen. In the Post-Proceedings of the 19th International Conference on Service-Oriented Computing (ICSOC). Springer. 2021. Invited Paper [pdf]
  • AuctionWhisk: Using an Auction-Inspired Approach for Function Placement in Serverless Fog Platforms. David Bermbach, Jonathan Bader, Jonathan Hasenburg, Tobias Pfandzelter, and Lauritz Thamsen. In Software: Practice and Experience 52 (5). Wiley. 2021. Journal Paper [pdf] [code]
  • On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds. Dominik Scheinert, Alireza Alamgiralem, Jonathan Bader, Jonathan Will, Thorsten Wittkopp, and Lauritz Thamsen. In the Proceedings of the 2021 IEEE International Conference on Big Data (Big Data). Presented at the 5th International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD). IEEE. 2021. Workshop Paper [pdf]
    • https://doi.org/10.1109/BigData52589.2021.9671275, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud. Jonathan Will, Onur Arslan, Jonathan Bader, Dominik Scheinert, and Lauritz Thamsen. In the Proceedings of the 2021 IEEE International Conference on Big Data (Big Data). Presented at the 5th International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD). IEEE. 2021. Workshop Paper [pdf] [video]
    • https://doi.org/10.1109/BigData52589.2021.9671742, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters. Jonathan Bader, Lauritz Thamsen, Svetlana Kulagina, Jonathan Will, Henning Meyerhenke, and Odej Kao. In the Proceedings of the 2021 IEEE International Conference on Big Data (Big Data). IEEE. 2021. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/BigData52589.2021.9671519, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. Philipp Wiesner, Ilja Behnke, Dominik Scheinert, Kordian Gontarska, and Lauritz Thamsen. In the Proceedings of the 22nd ACM/IFIP International Middleware Conference (Middleware). ACM. 2021. Results featured in a TU Berlin press release and a New Scientist article. Conference Paper [pdf] [video] [code]
    • © ACM, 2021. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the Middleware'21 Proceedings.
  • Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation. Dominik Scheinert, Houkun Zhu, Lauritz Thamsen, Morgan K. Geldenhuys, Jonathan Will, Alexander Acker, and Odej Kao. In the Proceedings of the 40th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2021. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/IPCCC51483.2021.9679361, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • On the Future of Cloud Engineering. David Bermbach, Abhishek Chandra, Chandra Krintz, Aniruddha Gokhale, Aleksander Slominski, Lauritz Thamsen, Everton Cavalcante, Tian Guo, Ivona Brandic, and Rich Wolski. In the Proceedings of the 9th IEEE International Conference on Cloud Engineering (IC2E). IEEE. 2021. Invited Paper [pdf]
    • https://doi.org/10.1109/IC2E52221.2021.00044, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Assessing the Resilience of Water Distribution Networks Under Different Sensor Network Architectures and Data Sampling Frequencies. Siling Chen, Florian Brokhausen, Philipp Wiesner, Lauritz Thamsen, and Andrea Cominola. In the Proceedings of the 2nd International Symposium on Water System Operations (ISWSO). IAHR. 2021. Extended Abstract [pdf]
  • GRAL: Localization of Floating Wireless Sensors in Pipe Networks. Martin Haug, Felix Lorenz, and Lauritz Thamsen. In the Proceedings of the 9th IEEE International Conference on Cloud Engineering (IC2E). Presented at the First International Workshop on Testing Distributed Internet of Things Systems (TDIS). IEEE. 2021. Workshop Paper [pdf] [code]
    • https://doi.org/10.1109/IC2E52221.2021.00042, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Dependable IoT Data Stream Processing for Monitoring and Control of Urban Infrastructures. Morgan Geldenhuys, Jonathan Will, Benjamin Pfister, Martin Haug, Alex Scharmann, and Lauritz Thamsen. In the Proceedings of the 9th IEEE International Conference on Cloud Engineering (IC2E). Presented at the First International Workshop on Testing Distributed Internet of Things Systems (TDIS). IEEE. 2021. Workshop Paper [pdf] [code]
    • https://doi.org/10.1109/IC2E52221.2021.00041, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts. Dominik Scheinert, Lauritz Thamsen, Houkun Zhu, Jonathan Will, Alexander Acker, Thorsten Wittkopp, and Odej Kao. In the Proceedings of the 23rd IEEE International Conference on Cluster Computing (Cluster). IEEE. 2021. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/Cluster48925.2021.00052, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • LOS: Local-Optimistic Scheduling of Periodic Model Training For Anomaly Detection on Sensor Data Streams in Meshed Edge Networks. Soeren Becker, Florian Schmidt, Lauritz Thamsen, Ana Juan Ferrer, and Odej Kao. In the Proceedings of the 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE. 2021. Conference Paper [pdf]
    • https://doi.org/10.1109/ACSOS52086.2021.00033, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Rafiki: Task-level Capacity Planning in Distributed Stream Processing Systems. Benjamin J. J. Pfister, Wolf S. Lickefett, Jan Nitschke, Sumit Paul, Morgan K. Geldenhuys, Dominik Scheinert, Kordian Gontarska, and Lauritz Thamsen. In the Proceedings of the Euro-Par 2021 Workshops (Euro-Par). Presented at the 3rd International Workshop on Parallel Programming Models in High-Performance Cloud (ParaMo). Springer. 2021. Workshop Paper [pdf] [code]
  • C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds. Jonathan Will, Lauritz Thamsen, Dominik Scheinert, Jonathan Bader, and Odej Kao. In the Proceedings of the 9th IEEE International Conference on Cloud Engineering (IC2E). IEEE. 2021. Conference Paper [pdf] [video] [code]
    • https://doi.org/10.1109/IC2E52221.2021.00018, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Evaluation of Load Prediction Techniques for Distributed Stream Processing. Kordian Gontarska, Morgan Geldenhuys, Dominik Scheinert, Philipp Wiesner, Andreas Polze, and Lauritz Thamsen. In the Proceedings of the 9th IEEE International Conference on Cloud Engineering (IC2E). IEEE. 2021. Short Conference Paper [pdf] [video]
    • https://doi.org/10.1109/IC2E52221.2021.00023, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems. Robert Danicki, Martin Haug, Ilja Behnke, Laurenz Mädje, and Lauritz Thamsen. In the Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking (EdgeSys), co-located with the 16th European Conference on Computer Systems (EuroSys). ACM. 2021. Workshop Paper [pdf]
    • © ACM, 2021. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the EdgeSys'21 Proceedings.
  • Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring. Kordian Gontarska, Weronika Wrazen, Jossekin Beilharz, Robert Schmid, Lauritz Thamsen, and Andreas Polze. Lecture Notes in Artificial Intelligence (LNAI). Presented at the 19th Conference on Artificial Intelligence in Medicine (AIME). Springer. 2021. Short Conference Paper [pdf]
  • Control Optimization Through Prediction-Based Wastewater Management. David Konstantin Tilcher, Florin Popescu, Harald Sommer, Lauritz Thamsen, and Paul Uwe Thamsen. In the 2021 ASME Conference Proceedings. Presented at the 2021 ASME Fluids Engineering Division Summer Meeting (FEDSM). ASME. 2021. Conference Paper
  • LEAF: Simulating Large Energy-Aware Fog Computing Environments. Philipp Wiesner and Lauritz Thamsen. In the Proceedings of the 5th IEEE International Conference on Fog and Edge Computing (ICFEC). IEEE. 2021. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/ICFEC51620.2021.00012, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms. Ana Juan Ferrer, Sören Becker, Florian Schmidt, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 21st IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGrid). Presented at the 1st Workshop on the Cloud-to-Things Continuum (Cloud2Things). IEEE. 2021. Workshop Paper [pdf]
    • https://doi.org/10.1109/CCGrid51090.2021.00076, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies. Dominik Scheinert, Alexander Acker, Lauritz Thamsen, Morgan K. Geldenhuys, and Odej Kao. In the Workshop Proceedings of the 43th International Conference on Software Engineering (ICSE Workshops). Presented at the 2nd Workshop on Cloud Intelligence (CloudIntelligence). IEEE. 2021. Workshop Paper [pdf]
    • © ACM, 2021. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the ICSE Workshops Proceedings.
  • PIERES: A Playground for Network Interrupt Experiments on Real-Time Embedded Systems in the IoT. Franz Bender, Jan Jonas Brune, Nick Lauritz Keutel, Ilja Behnke and Lauritz Thamsen. In the Companion of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE Companion). Presented at the 9th International Workshop on Load Testing and Benchmarking of Software Systems (LTB). ACM. 2021. Workshop Paper [pdf]
    • https://doi.org/10.1145/3447545.3451189, © ACM, 2021. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the ICPE Companion.
  • Towards a Staging Environment for the Internet of Things. Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer, Florian Brokhausen, Ilja Behnke, Robert Schmid, Lukas Pirl, and Lauritz Thamsen. In the Proceedings of the 19th IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Presented in the Work in Progress (WiP) session of the conference. IEEE. 2021. Best WiP Paper Award Work-in-Progress Paper [pdf] [code]
    • https://doi.org/10.1109/PerComWorkshops51409.2021.9431087, © IEEE 2021. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs. Morgan K. Geldenhuys, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). IEEE. 2020. Short Conference Paper [pdf]
    • https://doi.org/10.1109/BigData50022.2020.9378474, © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring. Felix Lorenz, Morgan Geldenhuys, Harald Sommer, Frauke Jakobs, Carsten Lüring, Volker Skwarek, Ilja Behnke, and Lauritz Thamsen. In the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). Presented at the Third International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2020. Workshop Paper [pdf] [code]
    • https://doi.org/10.1109/BigData50022.2020.9378138, © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs. Jonathan Will, Jonathan Bader, and Lauritz Thamsen. In the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). Presented at the 4th International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD). IEEE. 2020. Workshop Paper [pdf] [video] [data]
    • https://doi.org/10.1109/BigData50022.2020.9377994, © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet?. Ilja Behnke, Lukas Pirl, Lauritz Thamsen, Robert Danicki, Andreas Polze, and Odej Kao. In the Proceedings of the 39th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2020. Short Conference Paper [pdf] [video]
    • https://doi.org/10.1109/IPCCC50635.2020.9391536, © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Mary, Hugo, and Hugo*: Learning to Schedule Distributed Data-Parallel Processing Jobs on Shared Clusters. Lauritz Thamsen, Jossekin Beilharz, Vinh Thuy Tran, Sasho Nedelkoski, and Odej Kao. In Concurrency and Computation: Practice and Experience 33 (18). Wiley. 2020. Journal Paper [pdf] [code]
  • Fingerprinting Analog IoT Sensors for Secret-Free Authentication. Felix Lorenz, Lauritz Thamsen, Andreas Wilke, Ilja Behnke, Jens Waldmüller-Littke, Ilya Komarov, Odej Kao, and Manfred Paeschke. In the Workshop Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN). Presented at the 10th International Workshop on Security, Privacy, Trust, and Machine Learning for Internet of Things (IoTSPT-ML). IEEE. 2020. Workshop Paper [pdf]
    • https://doi.org/10.1109/ICCCN49398.2020.9209643, © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Effectively Testing System Configurations of Critical IoT Analytics Pipelines. Morgan K. Geldenhuys, Lauritz Thamsen, Kain Kordian Gontarska, Felix Lorenz, and Odej Kao. In the Proceedings of the 2019 IEEE International Conference on Big Data (BigData). Presented at the Second International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2019. Workshop Paper [pdf]
    • https://doi.org/BigData47090.2019.9005504, © IEEE 2019. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds. Ilja Behnke, Lauritz Thamsen, and Odej Kao. In the Companion of the 12th IEEE/ACM International Conference on Utility and Cloud Computing (UCC Companion). Presented at the 8th International Workshop on Cloud and Edge Computing and Applications Management (CloudAM). ACM. 2019. Workshop Paper [pdf] [code]
    • © ACM, 2019. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the UCC Companion.
  • Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs. Lauritz Thamsen, Ilya Verbitskiy, Sasho Nedelkoski, Vinh Thuy Tran, Vinícius Meyer, Miguel G. Xavier, Odej Kao, and César A. F. De Rose. In the Proceedings of the Euro-Par 2019 Workshops (Euro-Par). Presented at the 1st International Workshop on Parallel Programming Models in High-Performance Cloud (ParaMo). Springer. 2019. Workshop Paper [pdf] [code]
  • Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures. Sasho Nedelkoski, Lauritz Thamsen, Ilya Verbitskiy, and Odej Kao. In the Proceedings of the 2019 IEEE International Conference on Edge Computing (EDGE). IEEE. 2019. Conference Paper [pdf]
    • https://doi.org/10.1109/EDGE.2019.00015, © IEEE 2019. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters. Ilya Verbitskiy, Lauritz Thamsen, Thomas Renner, and Odej Kao. In the Proceedings of the 10th IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE. 2018. Conference Paper [pdf]
    • https://doi.org/10.1109/CloudCom2018.2018.00029, © IEEE 2018. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources. Gerrit Janßen, Ilya Verbitskiy, Thomas Renner, and Lauritz Thamsen. In the Proceedings of the 2018 IEEE International Conference on Big Data (BigData). Presented at the First International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2018. Workshop Paper [pdf]
    • https://doi.org/10.1109/BigData.2018.8622651, © IEEE 2018. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Learning Efficient Co-locations for Scheduling Distributed Dataflows in Shared Clusters. Lauritz Thamsen, Ilya Verbitskiy, Benjamin Rabier, and Odej Kao. In Services Transactions on Big Data 4 (1). Services Society. 2018. Journal Paper [pdf] [code]
  • Adaptive Resource Management for Distributed Data Analytics. Lauritz Thamsen, Thomas Renner, Ilya Verbitskiy, and Odej Kao. In Lucio Grandinetti, Seyedeh Leili Mirtaheri, Reza Shahbazian, Thomas Sterling, Vladimir Voevodin (eds.), Advances in Parallel Computing – Big Data and HPC: Ecosystem and Convergence. IOS Press. 2018. Book Chapter [pdf]
  • Ellis: Dynamically Scaling Distributed Dataflows to Meet Runtime Targets. Lauritz Thamsen, Ilya Verbitskiy, Jossekin Beilharz, Thomas Renner, Andreas Polze, and Odej Kao. In the Proceedings of the 9th IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE. 2017. Best Paper Candidate Conference Paper [pdf] [code]
    • https://doi.org/10.1109/CloudCom.2017.37, © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows. Jannis Koch, Lauritz Thamsen, Florian Schmidt, and Odej Kao. In the Proceedings of the 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE. 2017. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/PDCAT.2017.00034, © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference. Lauritz Thamsen, Benjamin Rabier, Florian Schmidt, Thomas Renner, and Odej Kao. In the Proceedings of the 6th IEEE International Congress on Big Data (BigData Congress). IEEE. 2017. Conference Paper [pdf] [code]
    • https://doi.org/10.1109/BigDataCongress.2017.28, © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Adaptive Resource Management for Distributed Data Analytics Based on Container-level Cluster Monitoring. Thomas Renner, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA). SCITEPRESS. 2017. Conference Paper [pdf] [code]
    • © SCITEPRESS, 2017. This contribution was presented at DATA 17. This is the authors' version of the work.
  • Addressing Hadoop’s Small File Problem With an Appendable Archive File Format. Thomas Renner, Johannes Müller, Lauritz Thamsen, and Odej Kao. In the Proceedings of the Big Data Analytics Workshop (BigDAW), co-located with the ACM International Conference on Computing Frontiers (CF). ACM. 2017. Workshop Paper [pdf] [code]
    • © ACM, 2017. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the proceedings of the Big Data Analytics Workshop (BigDAW17).
  • Selecting Resources for Distributed Dataflow Systems According to Runtime Targets. Lauritz Thamsen, Ilya Verbitskiy, Florian Schmidt, Thomas Renner, and Odej Kao. In the Proceedings of the 35th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2016. Conference Paper [pdf] [code] [data]
    • https://doi.org/10.1109/PCCC.2016.7820629, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • CoLoc: Distributed Data and Container Colocation for Data-Intensive Applications. Thomas Renner, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 2016 IEEE International Conference on Big Data (BigData). Presented at the 4th International Workshop on Distributed Storage Systems and Coding for Big Data. IEEE. 2016. Workshop Paper [pdf]
    • https://doi.org/10.1109/BigData.2016.7840954, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Visually Programming Dataflows for Distributed Data Analytics. Lauritz Thamsen, Thomas Renner, Marvin Byfeld, Markus Paeschke, Daniel Schröder, and Felix Böhm. In the Proceedings of the 2016 IEEE International Conference on Big Data (BigData). Presented at the 3rd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH). IEEE. 2016. Workshop Paper [pdf] [code]
    • https://doi.org/10.1109/BigData.2016.7840860, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • When to Use a Distributed Dataflow Engine: Evaluating the Performance of Apache Flink. Ilya Verbitskiy, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 2nd IEEE International Conference on Cloud and Big Data Computing (CBDCom). IEEE. 2016. Conference Paper [pdf]
    • https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0114, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Continuously Improving the Resource Utilization of Iterative Parallel Dataflows. Lauritz Thamsen, Thomas Renner, and Odej Kao. In the Proceedings of the 36th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW). Presented at the 6th International Workshop on Big Data and Cloud Performance (DCPerf). IEEE. 2016. Workshop Paper [pdf] [code]
    • http://dx.doi.org/10.1109/ICDCSW.2016.20, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Aura: A Flexible Dataflow Engine for Scalable Data Processing. Tobias Herb, Lauritz Thamsen, Thomas Renner, and Odej Kao. In Andreas Knüpfer, Tobias Hilbrich, Christoph Niethammer, José Gracia, Wolfgang E. Nagel, Michael M. Resch (eds.), Tools for High Performance Computing 2015. Springer. 2016. Book Chapter [pdf] [code]
  • Exploratory Authoring of Interactive Content in a Live Environment. Philipp Otto, Jaqueline Pollak, Daniel Werner, Felix Wolff, Bastian Steinert, Lauritz Thamsen, Marcel Taeumel, Jens Lincke, Robert Krahn, Daniel H. H. Ingalls, and Robert Hirschfeld. HPI Technical Reports, vol. 101. Hasso Plattner Institute. 2016. Technical Report [pdf]
  • Lively Groups: Shared Behavior in a World of Objects without Classes or Prototypes. Tim Felgentreff, Jens Lincke, Robert Hirschfeld, and Lauritz Thamsen. In the Proceedings of the Future Programming Workshop (FPW), co-located with the Conference on Object-oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM. 2015. Workshop Paper [pdf]
    • © ACM, 2015. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the proceedings of the Future Programming Workshop (FPW) 2015.
  • Network-Aware Resource Management for Scalable Data Analytics Frameworks. Thomas Renner, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 2015 IEEE International Conference on Big Data (BigData). Presented in the 1st Workshop on Data-Centric Infrastructure for Big Data Science (DIBS). IEEE. 2015. Workshop Paper [pdf] [code]
    • http://dx.doi.org/10.1109/BigData.2015.7364083, © IEEE 2015. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Preserving Access to Previous System States in the Lively Kernel. Lauritz Thamsen, Bastian Steinert, and Robert Hirschfeld. In Hasso Plattner, Christoph Meinel, and Larry Leifer (eds.), Design Thinking Research: Making Design Thinking Foundational. Springer. 2015. Book Chapter [pdf] [code]
  • Implicit Parallelism through Deep Language Embedding. Alexander Alexandrov, Andreas Kunft, Asterios Katsifodimos, Felix Schüler, Lauritz Thamsen, Odej Kao, Tobias Herb, and Volker Markl. In the Proceedings of the 36th ACM SIGMOD International Conference on Management of Data (SIGMOD). ACM. 2015. SIGMOD Research Highlight Conference Paper [pdf] [project]
    • © ACM, 2015. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the proceedings of the ACM SIGMOD international conference.
  • Object Versioning to Support Recovery Needs: Using Proxies to Preserve Previous Development States in Lively. Bastian Steinert, Lauritz Thamsen, Tim Felgentreff, and Robert Hirschfeld. In the Proceedings of the 10th Dynamic Languages Symposium (DLS), co-located with the Conference on Object-oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM. 2014. Conference Paper [pdf] [code]
    • © ACM, 2014. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in the proceedings of the Dynamic Languages Symposium.
  • Orca: A Single-language Web Framework for Collaborative Development. Lauritz Thamsen, Anton Gulenko, Michael Perscheid, Robert Krahn, Robert Hirschfeld, and David A. Thomas. In the Proceedings of the 10th International Conference on Creating, Connecting and Collaborating through Computing (C5). IEEE. 2012. Conference Paper [pdf] [project]
    • http://dx.doi.org/10.1109/C5.2012.9, © IEEE 2012. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
theses:
  • Dynamic Resource Allocation for Distributed Dataflows. Lauritz Thamsen. PhD thesis submitted at TU Berlin in March 2018 and successfully defended on May 4, 2018. PhD Thesis [pdf] [slides]
  • Object Versioning for the Lively Kernel: Preserving Access to Previous System States in an Object-oriented Programming System. Lauritz Thamsen. Master thesis submitted at Hasso-Plattner-Institut, University of Potsdam, in May 2014. Master Thesis [pdf] [slides] [code]
  • Object Collaboration in the Orca Web Framework. Lauritz Thamsen. Bachelor thesis submitted at Hasso-Plattner-Institut, University of Potsdam, in June 2011. Bachelor Thesis [pdf] [project]