activity
20212023
most citedLet's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud

109 citations · 173 across the 13 of their papers we have counts for

collaborators

15 papers

cs.DC2023

Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients

Pratik Agrawal, Philipp Wiesner, Odej Kao

Federated Learning (FL) is a decentralized machine learning approach where local models are trained on distributed clients, allowing privacy-preserving collaboration by sharing mod…

cs.DC2023★ 4 cited

Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics

Dominik Scheinert, Philipp Wiesner, Thorsten Wittkopp +3

Selecting the right resources for big data analytics jobs is hard because of the wide variety of configuration options like machine type and cluster size. As poor choices can have…

cs.DC2023★ 2 cited

Vessim: A Testbed for Carbon-Aware Applications and Systems

Philipp Wiesner, Ilja Behnke, Paul Kilian +2

To reduce the carbon footprint of computing and stabilize electricity grids, there is an increasing focus on approaches that align the power usage of IT infrastructure with the ava…

cs.NI2023★ 9 cited

Towards a Real-Time IoT: Approaches for Incoming Packet Processing in Cyber-Physical Systems

Ilja Behnke, Christoph Blumschein, Robert Danicki +3

Embedded real-time devices for monitoring, controlling, and collaboration purposes in cyber-physical systems are now commonly equipped with IP networking capabilities. However, the…

cs.LG2023★ 31 cited

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Philipp Wiesner, Ramin Khalili, Dennis Grinwald +3

Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably…

cs.LG2023

PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning

Thorsten Wittkopp, Dominik Scheinert, Philipp Wiesner +2

Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data suc…

cs.DC2022★ 14 cited

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…

cs.NI2022★ 3 cited

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…

cs.DC2021

Continuously Testing Distributed IoT Systems: An Overview of the State of the Art

Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer +6

The continuous testing of small changes to systems has proven to be useful and is widely adopted in the development of software systems. For this, software is tested in environment…

cs.DB2021

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…

cs.LG2021

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…

cs.DC2021★ 109 cited

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…

cs.DC2021★ 1 cited

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…

cs.DC2021

LEAF: Simulating Large Energy-Aware Fog Computing Environments

Philipp Wiesner, Lauritz Thamsen

Despite constant improvements in efficiency, today's data centers and networks consume enormous amounts of energy and this demand is expected to rise even further. An important res…

cs.DC2021

Towards a Staging Environment for the Internet of Things

Jossekin Beilharz, Philipp Wiesner, Arne Boockmeyer +5

Internet of Things (IoT) applications promise to make many aspects of our lives more efficient and adaptive through the use of distributed sensing and computing nodes. A central as…