31 citations · 407 across the 77 of their papers we have counts for
41 papers · 1 filter
Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations
Miray Özcan, Philipp Wiesner, Philipp Weiß +1
The environmental impact of Large Language Models (LLMs) is rising significantly, with inference now accounting for more than half of their total lifecycle carbon emissions. Howeve…
Choosing the Right Battery Model for Data Center Simulations
Paul Kilian, Philipp Wiesner, Odej Kao
As demand for computing resources continues to rise, the increasing cost of electricity and anticipated regulations on carbon emissions are prompting changes in data center power s…
Carbon-Aware Quality Adaptation for Energy-Intensive Services
Philipp Wiesner, Dennis Grinwald, Philipp Weiß +3
The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. To date, carbon-aware computing strategies have pri…
On Software Ageing Indicators in OpenStack
Yevhen Yazvinskyi, Jasmin Bogatinovski, Jorge Cardoso +1
Distributed systems in general and cloud systems in particular, are susceptible to failures that can lead to substantial economic and data losses, security breaches, and even poten…
Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of Distributed Stream Processing Systems
Benjamin J. J. Pfister, Dominik Scheinert, Morgan K. Geldenhuys +1
Distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of S…
Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization
Morgan Geldenhuys, Dominik Scheinert, Odej Kao +1
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamica…
Predicting Dynamic Memory Requirements for Scientific Workflow Tasks
Jonathan Bader, Nils Diedrich, Lauritz Thamsen +1
With the increasing amount of data available to scientists in disciplines as diverse as bioinformatics, physics, and remote sensing, scientific workflow systems are becoming increa…
Carbon-Awareness in CI/CD
Henrik Claßen, Jonas Thierfeldt, Julian Tochman-Szewc +2
While the environmental impact of digitalization is becoming more and more evident, the climate crisis has become a major issue for society. For instance, data centers alone accoun…
Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures
Jonathan Bader, Fabian Lehmann, Lauritz Thamsen +2
Many resource management techniques for task scheduling, energy and carbon efficiency, and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime…
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…
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…
Evaluation of Data Enrichment Methods for Distributed Stream Processing Systems
Dominik Scheinert, Fabian Casares, Morgan K. Geldenhuys +2
Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Th…
Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?
Jonathan Will, Lauritz Thamsen, Dominik Scheinert +1
Distributed dataflow systems such as Apache Spark or Apache Flink enable parallel, in-memory data processing on large clusters of commodity hardware. Consequently, the appropriate…
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…
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…
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…
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…
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…