109 citations · 463 across the 69 of their papers we have counts for
26 papers · 1 filter
Continuously Testing Distributed IoT Systems: An Overview of the State of the Art
Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer +6
The continuous testing of small changes to systems has proven to be useful and is widely adopted in the development of software systems. For this, software is tested in environment…
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds
Dominik Scheinert, Alireza Alamgiralem, Jonathan Bader +3
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important. Since managing a dedicated infrastructure is i…
Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud
Jonathan Will, Onur Arslan, Jonathan Bader +2
Distributed dataflow systems like Apache Flink and Apache Spark simplify processing large amounts of data on clusters in a data-parallel manner. However, choosing suitable cluster…
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…
Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud
Philipp Wiesner, Ilja Behnke, Dominik Scheinert +2
Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emiss…
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…
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…
GRAL: Localization of Floating Wireless Sensors in Pipe Networks
Martin Haug, Felix Lorenz, Lauritz Thamsen
Mobile wireless sensors are increasingly recognized as a valuable tool for monitoring critical infrastructures. An important use case is the discovery of leaks and inflows in pipe…
Dependable IoT Data Stream Processing for Monitoring and Control of Urban Infrastructures
Morgan K. Geldenhuys, Jonathan Will, Benjamin J. J. Pfister +3
The Internet of Things describes a network of physical devices interacting and producing vast streams of sensor data. At present there are a number of general challenges which exis…
On the Future of Cloud Engineering
David Bermbach, Abhishek Chandra, Chandra Krintz +7
Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many diffe…
Evaluation of Load Prediction Techniques for Distributed Stream Processing
Kordian Gontarska, Morgan Geldenhuys, Dominik Scheinert +3
Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-inte…
AuctionWhisk: Using an Auction-Inspired Approach for Function Placement in Serverless Fog Platforms
David Bermbach, Jonathan Bader, Jonathan Hasenburg +2
The Function-as-a-Service (FaaS) paradigm has a lot of potential as a computing model for fog environments comprising both cloud and edge nodes, as compute requests can be schedule…
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…
C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds
Jonathan Will, Lauritz Thamsen, Dominik Scheinert +2
Distributed dataflow systems enable data-parallel processing of large datasets on clusters. Public cloud providers offer a large variety and quantity of resources that can be used…
Predicting Medical Interventions from Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring
Kordian Gontarska, Weronika Wrazen, Jossekin Beilharz +3
Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical t…
Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems
Robert Danicki, Martin Haug, Ilja Behnke +2
Manufacturing, automotive, and aerospace environments use embedded systems for control and automation and need to fulfill strict real-time guarantees. To facilitate more efficient…
Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms
Ana Juan Ferrer, Soeren Becker, Florian Schmidt +2
In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collabor…
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…
LEAF: Simulating Large Energy-Aware Fog Computing Environments
Philipp Wiesner, Lauritz Thamsen
Despite constant improvements in efficiency, today's data centers and networks consume enormous amounts of energy and this demand is expected to rise even further. An important res…
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…
PIERES: A Playground for Network Interrupt Experiments on Real-Time Embedded Systems in the IoT
Franz Bender, Jan Jonas Brune, Nick Lauritz Keutel +2
IoT devices have become an integral part of our lives and the industry. Many of these devices run real-time systems or are used as part of them. As these devices receive network pa…
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…
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…