23 citations · 49 across the 12 of their papers we have counts for
12 papers
Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of Distributed Stream Processing Systems
Benjamin J. J. Pfister, Dominik Scheinert, Morgan K. Geldenhuys +1
Distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of S…
Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization
Morgan Geldenhuys, Dominik Scheinert, Odej Kao +1
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamica…
Evaluation of Data Enrichment Methods for Distributed Stream Processing Systems
Dominik Scheinert, Fabian Casares, Morgan K. Geldenhuys +2
Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Th…
Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads
Morgan K. Geldenhuys, Dominik Scheinert, Odej Kao +1
Distributed Stream Processing systems have become an essential part of big data processing platforms. They are characterized by the high-throughput processing of near to real-time…
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…
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…
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…
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…
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…
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…
A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring
Felix Lorenz, Morgan Geldenhuys, Harald Sommer +5
With weather becoming more extreme both in terms of longer dry periods and more severe rain events, municipal water networks are increasingly under pressure. The effects include da…