109 citations · 463 across the 69 of their papers we have counts for
13 papers · 1 filter
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments
Dominik Scheinert, Babak Sistani Zadeh Aghdam, Soeren Becker +2
With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is…
Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics
Dominik Scheinert, Soeren Becker, Jonathan Bader +3
Choosing a good resource configuration for big data analytics applications can be challenging, especially in cloud environments. Automated approaches are desirable as poor decision…
Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing
Jonathan Will, Lauritz Thamsen, Jonathan Bader +2
Selecting appropriate computational resources for data processing jobs on large clusters is difficult, even for expert users like data engineers. Inadequate choices can result in v…
Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures
Jonathan Bader, Fabian Lehmann, Alexander Groth +5
Scientific workflows typically comprise a multitude of different processing steps which often are executed in parallel on different partitions of the input data. These executions,…
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning
Houkun Zhu, Dominik Scheinert, Lauritz Thamsen +2
Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal. At the same time, tuning configuration parameters is typically chall…
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…
Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing
Jonathan Will, Lauritz Thamsen, Jonathan Bader +2
Distributed dataflow systems like Apache Spark and Apache Hadoop enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources f…
Collaborative Cluster Configuration for Distributed Data-Parallel Processing: A Research Overview
Lauritz Thamsen, Dominik Scheinert, Jonathan Will +2
Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequat…
Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters
Jonathan Bader, Fabian Lehmann, Lauritz Thamsen +3
Many scientific workflow scheduling algorithms need to be informed about task runtimes a-priori to conduct efficient scheduling. In heterogeneous cluster infrastructures, this prob…
Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads
Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp +2
The growing electricity demand of cloud and edge computing increases operational costs and will soon have a considerable impact on the environment. A possible countermeasure is equ…
Differentiating Network Flows for Priority-Aware Scheduling of Incoming Packets in Real-Time IoT Systems
Christoph Blumschein, Ilja Behnke, Lauritz Thamsen +1
When IP-packet processing is unconditionally carried out on behalf of an operating system kernel thread, processing systems can experience overload in high incoming traffic scenari…
SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed Edge Networks
Daniel Habenicht, Kevin Kreutz, Soeren Becker +3
The increasing use of Internet of Things devices coincides with more communication and data movement in networks, which can exceed existing network capabilities. These devices ofte…
A Priority-Aware Multiqueue NIC Design
Ilja Behnke, Philipp Wiesner, Robert Danicki +1
Low-level embedded systems are used to control cyber-phyiscal systems in industrial and autonomous applications. They need to meet hard real-time requirements as unanticipated cont…