31 citations · 407 across the 77 of their papers we have counts for
11 papers · 2 filters
Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters
Jonathan Bader, Lauritz Thamsen, Svetlana Kulagina +3
Scientific workflow management systems like Nextflow support large-scale data analysis by abstracting away the details of scientific workflows. In these systems, workflows consist…
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 +2
Anomaly detection is increasingly important to handle the amount of sensor data in Edge and Fog environments, Smart Cities, as well as in Industry 4.0. To ensure good results, the…
EdgePier: P2P-based Container Image Distribution in Edge Computing Environments
Soeren Becker, Florian Schmidt, Odej Kao
Edge and fog computing architectures utilize container technologies in order to offer a lightweight application deployment. Container images are stored in registry services and ope…
Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing
Morgan K. Geldenhuys, Benjamin J. J. Pfister, Dominik Scheinert +2
Distributed Stream Processing systems are becoming an increasingly essential part of Big Data processing platforms as users grow ever more reliant on their ability to provide fast…
Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation
Dominik Scheinert, Houkun Zhu, Lauritz Thamsen +4
Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics. While runtime prediction models can be used to initially select appropriat…
Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts
Dominik Scheinert, Lauritz Thamsen, Houkun Zhu +4
Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforw…
Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Dominik Scheinert, Alexander Acker, Lauritz Thamsen +2
Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur. Utilizi…
Effectively Testing System Configurations of Critical IoT Analytics Pipelines
Morgan Geldenhuys, Lauritz Thamsen, Kain Kordian Gontarska +2
The emergence of the Internet of Things has seen the introduction of numerous connected devices used for the monitoring and control of even Critical Infrastructures. Distributed st…
Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs
Lauritz Thamsen, Ilya Verbitskiy, Sasho Nedelkoski +5
Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for analysis of large datasets with cluster resources. Yet, users often overprovision resourc…
Towards AIOps in Edge Computing Environments
Soeren Becker, Florian Schmidt, Anton Gulenko +2
Edge computing was introduced as a technical enabler for the demanding requirements of new network technologies like 5G. It aims to overcome challenges related to centralized cloud…
Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs
Morgan Geldenhuys, Lauritz Thamsen, Odej Kao
Fault tolerance is a property which needs deeper consideration when dealing with streaming jobs requiring high levels of availability and low-latency processing even in case of fai…