Research on Adaptive Resource Management (ARM)

ARM is a close collaboration in research between Technische Universität Berlin (TUB) and University of Glasgow (UofG), focusing on adaptive resource management for data-intensive applications. The collaborative effort at the intersection of distributed systems, operating systems, and information systems was started in 2019 in Berlin. It is led by Prof. Dr. Odej Kao (TUB) and Dr. Lauritz Thamsen (UofG).

Our goal is to develop new methods, systems, and tools that make it easier to run data-intensive applications in a resource-efficient and resilient manner. Towards this goal, we work on adaptive resource management for distributed computing environments, from small IoT devices to large clusters of cloud resources. Ultimately, we aim to realize systems that automatically adapt to workloads, computing environments, and application requirements.

Team

News

12.04.24 Invited talk at HPI Research Symposium 2024: Lauritz Thamsen will summarize our work on carbon-aware edge/cloud computing at the HPI Research Symposium 2024 at Hasso Plattner Institute in Potsdam.
28.03.24 Funded PhD opportunity at UofG: There is a scholarship to be allocated – covering fees for UK students (and settled/pre-settled EU students) as well as a UKRI rate stipend for four years – for an exciting PhD project on sustainable data services with Barclays at Glasgow.
03.11.23 Paper at e-Energy 2024: Our paper on a low-carbon federated learning system — operating exclusively on renewable excess energy and spare compute capacity — was accepted at ACM e-Energy 2024.
11.10.23 SPE Special Issue: We contributed to a special issue on Benchmarking, Experimentation Tools, and Reproducible Practices for Data‐Intensive Systems from Edge to Cloud that was published in Wiley’s Software: Practice and Experience journal.
03.07.23 Invited talk at Cardiff University Lauritz Thamsen presented some of our work on carbon-aware computing at the Cardiff University workshop on Flexible and Zero Carbon Energy Systems for Data Centers (#1, #2, #3).
15.03.23 Paper at CCGrid 2023: Our work on better integrating workflow engines with cluster resource managers was accepted at CCGrid 2023. The paper is the result of a collaboration with Ulf Leser’s group at Humboldt University of Berlin in context of the Collaborative Research Center FONDA.
20.02.23 Invited talk at Kent University Lauritz Thamsen presented some of our recent work on carbon-aware scheduling for flexible cloud and edge workloads at the University of Kent (slides).
04.11.22 New DFG project funded: Our project proposal on C5: Collaborative and Cross-Context Cluster Configuration for Distributed Data-Parallel Processing was accepted for funding by the German Research Foundation. The project will run for three years, with funding for one RA position. This will allow us to continue our work on the topic.
23.10.22 GC₃ lab: Lauritz Thamsen’s new computer systems lab at the University of Glasgow focuses on Carbon-Conscious Computing.
30.09.22 Contributions to IC2E 2022: We helped put together the technical program for the 10th IEEE International Conference on Cloud Engineering (IC2E) and also presented two full papers at the conference: a new approach to profiling the memory needs of distributed dataflow programs and work on parameter tuning for distributed file systems.
27.09.22 Organization of TDIS 2022: Together with colleagues from the Hasso Plattner Institute and University of Nicosia, we organized the 2nd International Workshop on Testing Distributed Internet of Things Systems (TDIS), which took place with IC2E 2022 and was attended by around 25 participants in Pacific Grove, California.
22.08.22 Contributions to Euro-Par 2022: We helped to run the 28th International European Conference on Parallel and Distributed Computing (Euro-Par) in Glasgow and also presented recent work on renewable-aware edge computing in the session on Cluster and Cloud Computing.
31.07.22 Research Stay at Glasgow: Philipp Wiesner completed a research stay of four months as a guest of the Glasgow Systems Section at the University of Glasgow.
15.07.22 Best paper at ICWS 2022: Our paper Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads was accepted and presented at the 19th International Conference on Web Services (IEEE ICWS 2022) in Barcelona, Spain. The work was presented with the award for best paper at the conference.
10.06.22 BIFOLD special issue article: We summarized our research on collaborative cluster configuration optimization in a special BIFOLD issue of the German Informatics Society’s “Datenbank-Spektrum”.
09.05.22 CfP: 2nd TDIS Workshop: The call for the 2nd International Workshop on Testing Distributed Internet of Things Systems (TDIS) co-located with the 10th IEEE International Conference on Cloud Engineering is now open. Submit your contribution until June 21, 2022!
01.03.22 Lauritz Thamsen moved to University of Glasgow: Lauritz Thamsen started as a Lecturer in Computer Systems in the University of Glasgow’s School of Computing Science, making ARM a collaboration between researchers at TU Berlin and UofG now.
06.12.21 Paper at Middleware 2021: Our paper Let’s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud was accepted for presentation at Middleware’21. The paper was also featured in an article in a New Scientist article as well as a BIFOLD press release.
30.11.21 Organization of ICSOC 2021: We helped to organize the 19th International Conference on Service-Oriented Computing (ICSOC), which was held virtually from November 22 to 25 with paper presentations, workshops, tutorials, demonstrations, and a PhD symposium on topics including distributed systems, cloud/edge/fog computing, cyber-physical systems, scientific workflows, data science, and software engineering.
04.10.21 1st TDIS Workshop at IEEE IC2E 2021: Our 1st International Workshop on Testing Distributed Internet of Things Systems (TDIS) has been held online with two invited talks, six paper presentations, and lively discussions between up to 32 participants. The workshop was co-located with the 9th IEEE International Conference on Cloud Engineering.

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Publications

2024

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). 2024. [arXiv preprint] [code]
Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling. Jonathan Will, Dominik Scheinert, Jan Bode, Cedric Kring, Seraphin Zunzer, Lauritz Thamsen. In Companion of the 2024 ACM/SPEC International Conference on Performance Engineering (ICPE). 2024. [arXiv preprint] [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. ACM. 2024. [Open Access]
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 15th ACM International Conference on Future and Sustainable Energy Systems (e-Energy). ACM. 2024. [arXiv preprint] [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. [arXiv preprint] [code]

2023

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. [arXiv preprint] [code]
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. [arXiv preprint] [code]
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. [arXiv preprint]
Real-Time Performance of Industrial IoT Communication Technologies: A Review. Ilja Behnke and Henrik Austad. In IEEE Internet of Things Journal, 11(5). 2023. [arXiv preprint]
Carbon-Awareness in CI/CD. Henrik Claßen, Jonas Thierfeldt, Julian Tochman-Szewc, Philipp Wiesner, and Odej Kao. In the Proceedings of the 1st International Workshop on Sustainable Service-Oriented Computing (SSCOPE) at ICSOC ‘23. Springer. 2023. [arXiv preprint]
Offloading Real-Time Tasks in IIoT Environments under Consideration of Networking Uncertainties. Ilja Behnke, Philipp Wiesner, Paul Voelker, and Odej Kao. In the Proceedings of the 2nd International Workshop on Middleware for the Edge (MiddleWEdge) at Middleware ‘23. ACM. 2023. [arXiv preprint]
Software-in-the-Loop Simulation for Developing and Testing Carbon-Aware Applications. Philipp Wiesner, Marvin Steinke, Henrik Nickel, Yazan Kitana, and Odej Kao. In Software: Practice and Experience 53(12). Wiley. 2023. [Open Access] [code]
Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients. Pratik Agrawal, Philipp Wiesner, and Odej Kao. In the Proceedings of the 2nd Workshop on Machine Learning & Networking (MaLeNe) at NetSys ‘23. 2023. [arXiv preprint]
Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings. Demetris Trihinas and Lauritz Thamsen. In the Proceedings of the Euro-Par 2023 Workshops and Minisymposia (Euro-Par). Springer. 2023.
Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?. Jonathan Will, Lauritz Thamsen, Dominik Scheinert, Odej Kao. In the Proceedings of the 35th International Conference on Scientific and Statistical Database Management (SSDBM). ACM. 2023. [arXiv preprint]
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. [arXiv preprint]
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 23nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE. 2023. [arXiv preprint] [code]
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. [arXiv preprint] [code]

2022

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. [arXiv preprint]
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. [arXiv preprint] [code]
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 IEEE International Conference on Big Data (BigData). IEEE. 2022. [arXiv preprint] [code]
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 41th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2022. [arXiv preprint] [code]
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. [arXiv preprint]
IoTreeplay: Synchronous Distributed Traffic Replay in IoT Environments. Markus Toll, Ilja Behnke, and Odej Kao. 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. [arXiv preprint]
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. [arXiv preprint] [code]
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. [arXiv preprint] [code]
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. [arXiv preprint] [code]
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. [arXiv preprint] [code]
Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads. Morgan Geldenhuys, Dominik Scheinert, Odej Kao, and Lauritz Thamsen. In the Proceedings of the IEEE 2022 International Conference on Web Services (ICWS). IEEE. 2022. [arXiv preprint] [code]
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. [Open Access]
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. [arXiv preprint] [arXiv preprint]
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. [arXiv preprint]
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 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. [arXiv preprint] [arXiv preprint]
The Methods of Cloud Computing. Lauritz Thamsen, Jossekin Beilharz, Andreas Polze, and Odej Kao. Technical Report. Technische Universität Berlin. 2022. [Open Access]
A Priority-Aware Multiqueue NIC Design. Ilja Behnke, Philipp Wiesner, Robert Danicki, and Lauritz Thamsen. In the Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (SAC ‘22). ACM. 2022. [arXiv preprint] [video]

2021

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. [Open Access] [code]
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. [arXiv preprint] [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. [arXiv preprint] [video]
Continuously Testing Distributed IoT Systems: An Overview of the State of the Art. Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer, Lukas Pirl, Dirk Friedenberger, Florian Brokhausen, Ilja Behnke, Andreas Polze, and Lauritz Thamsen. In the Post-Proceedings of the 19th International Conference on Service Oriented Computing (ICSOC). Springer. 2021. [arXiv preprint]
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. [arXiv preprint] [code]
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. [arXiv preprint]
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. [arXiv preprint] [code]
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. [arXiv preprint]
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. [arXiv preprint] [code]
LEAF: Simulating Large Energy-Aware Fog Computing Environments. Philipp Wiesner and Lauritz Thamsen. In the Proceedings of the 2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC). IEEE. 2021. [arXiv preprint] [video] [code]
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 International Middleware Conference (Middleware). ACM. 2021. [arXiv preprint] [code]
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.
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds. Dominik Scheinert, Alireza Alamgiralem, Jonathan Bader, Jonathan Will, Thorsten Wittkopp, 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. [arXiv preprint]
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). IEEE. 2021. ACM. 2021. [arXiv preprint]
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. [Google Scholar]
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. [arXiv preprint]
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. [arXiv preprint]
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. [arXiv preprint] [video]

2020

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 Second International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2020. [arXiv preprint]
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. [arXiv preprint]
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. [arXiv preprint]
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 International Performance Computing and Communications Conference (IPCCC’20). IEEE. 2020. [arXiv preprint] [video]
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. [Open Access] [code]
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. [arXiv preprint] [video] [data]

2019

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 (IEEE BigData). Presented at the Second International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2019. [Google Scholar]
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. [Google Scholar]
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. Springer. 2019. [Google Scholar] [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. [Google Scholar]