109 citations · 116 across the 4 of their papers we have counts for
7 papers
Efficient Runtime Profiling for Black-box Machine Learning Services on Sensor Streams
Soeren Becker, Dominik Scheinert, Florian Schmidt +1
In highly distributed environments such as cloud, edge and fog computing, the application of machine learning for automating and optimizing processes is on the rise. Machine learni…
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…
A2Log: Attentive Augmented Log Anomaly Detection
Thorsten Wittkopp, Alexander Acker, Sasho Nedelkoski +4
Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a…
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…
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…