31 citations · 407 across the 77 of their papers we have counts for
21 papers · 1 filter
First CE Matters: On the Importance of Long Term Properties on Memory Failure Prediction
Jasmin Bogatinovski, Qiao Yu, Jorge Cardoso +1
Dynamic random access memory failures are a threat to the reliability of data centres as they lead to data loss and system crashes. Timely predictions of memory failures allow for…
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things
Soeren Becker, Kevin Styp-Rekowski, Oliver Vincent Leon Stoll +1
Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient…
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…
Towards Advanced Monitoring for Scientific Workflows
Jonathan Bader, Joel Witzke, Soeren Becker +5
Scientific workflows consist of thousands of highly parallelized tasks executed in a distributed environment involving many components. Automatic tracing and investigation of the c…
Leveraging Reinforcement Learning for Task Resource Allocation in Scientific Workflows
Jonathan Bader, Nicolas Zunker, Soeren Becker +1
Scientific workflows are designed as directed acyclic graphs (DAGs) and consist of multiple dependent task definitions. They are executed over a large amount of data, often resulti…
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…
IoTreeplay: Synchronous Distributed Traffic Replay in IoT Environments
Markus Toll, Ilja Behnke, Odej Kao
Use-cases in the Internet of Things (IoT) typically involve a high number of interconnected, heterogeneous devices. Due to the criticality of many IoT scenarios, systems and applic…
Network Emulation in Large-Scale Virtual Edge Testbeds: A Note of Caution and the Way Forward
Soeren Becker, Tobias Pfandzelter, Nils Japke +2
The growing research and industry interest in the Internet of Things and the edge computing paradigm has increased the need for cost-efficient virtual testbeds for large-scale dist…
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…
Leveraging Log Instructions in Log-based Anomaly Detection
Jasmin Bogatinovski, Gjorgji Madjarov, Sasho Nedelkoski +2
Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e.g., anomaly…
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…
Failure Identification from Unstable Log Data using Deep Learning
Jasmin Bogatinovski, Sasho Nedelkoski, Li Wu +2
The reliability of cloud platforms is of significant relevance because society increasingly relies on complex software systems running on the cloud. To improve it, cloud providers…
Data-Driven Approach for Log Instruction Quality Assessment
Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker +2
In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messa…
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…