no code implementations • 22 Aug 2023 • Dominik Scheinert, Philipp Wiesner, Thorsten Wittkopp, Lauritz Thamsen, Jonathan Will, Odej Kao
However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks.
1 code implementation • 24 May 2023 • Philipp Wiesner, Ramin Khalili, Dennis Grinwald, Pratik Agrawal, Lauritz Thamsen, Odej Kao
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing.
no code implementations • 24 Nov 2022 • Dominik Scheinert, Babak Sistani Zadeh Aghdam, Soeren Becker, Odej Kao, Lauritz Thamsen
With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments.
no code implementations • 15 Nov 2022 • Dominik Scheinert, Soeren Becker, Jonathan Bader, Lauritz Thamsen, Jonathan Will, Odej Kao
Choosing a good resource configuration for big data analytics applications can be challenging, especially in cloud environments.
1 code implementation • 19 Jul 2022 • Houkun Zhu, Dominik Scheinert, Lauritz Thamsen, Kordian Gontarska, Odej Kao
Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal.
no code implementations • 16 Nov 2021 • Dominik Scheinert, Alireza Alamgiralem, Jonathan Bader, Jonathan Will, Thorsten Wittkopp, Lauritz Thamsen
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important.
1 code implementation • 27 Aug 2021 • Dominik Scheinert, Houkun Zhu, Lauritz Thamsen, Morgan K. Geldenhuys, Jonathan Will, Alexander Acker, Odej Kao
Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics.
1 code implementation • 10 Aug 2021 • Kordian Gontarska, Morgan Geldenhuys, Dominik Scheinert, Philipp Wiesner, Andreas Polze, Lauritz Thamsen
We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs.
1 code implementation • 29 Jul 2021 • Dominik Scheinert, Lauritz Thamsen, Houkun Zhu, Jonathan Will, Alexander Acker, Thorsten Wittkopp, Odej Kao
First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts.
no code implementations • 20 Apr 2021 • Kordian Gontarska, Weronika Wrazen, Jossekin Beilharz, Robert Schmid, Lauritz Thamsen, Andreas Polze
Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the appropriate treatment.
1 code implementation • 9 Mar 2021 • Dominik Scheinert, Alexander Acker, Lauritz Thamsen, Morgan K. Geldenhuys, Odej Kao
Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur.
no code implementations • 11 Feb 2021 • Morgan Geldenhuys, Lauritz Thamsen, Kain Kordian Gontarska, Felix Lorenz, Odej Kao
Distributed stream processing has become key to analyzing data generated by these connected devices and improving our ability to make decisions.
Distributed, Parallel, and Cluster Computing
no code implementations • 11 Feb 2021 • Morgan Geldenhuys, Lauritz Thamsen, Odej Kao
However, this is an expensive operation which impacts negatively on the overall performance of the system and manually optimizing fault tolerance for specific jobs is a difficult and time consuming task.
Distributed, Parallel, and Cluster Computing