109 citations · 258 across the 37 of their papers we have counts for
13 papers · 1 filter
A Taxonomy of Anomalies in Log Data
Thorsten Wittkopp, Philipp Wiesner, Dominik Scheinert +1
Log data anomaly detection is a core component in the area of artificial intelligence for IT operations. However, the large amount of existing methods makes it hard to choose the r…
LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision
Thorsten Wittkopp, Philipp Wiesner, Dominik Scheinert +1
With increasing scale and complexity of cloud operations, automated detection of anomalies in monitoring data such as logs will be an essential part of managing future IT infrastru…
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…
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…
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…
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…
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…
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…
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services
Dominik Scheinert, Alexander Acker
Deployment, operation and maintenance of large IT systems becomes increasingly complex and puts human experts under extreme stress when problems occur. Therefore, utilization of ma…