109 citations · 258 across the 37 of their papers we have counts for
38 papers
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression
Sasho Nedelkoski, Alexander Acker, Soeren Becker +2
Compressing the key-value (KV) cache is essential for serving long-context large language models (LLMs) under memory constraints. While current scalar quantization methods achieve…
Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process
Houkun Zhu, Helena Ebel, Dominik Scheinert +3
Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas turbine component Product Lifecyc…
Beyond Microservices: Testing Web-Scale RCA Methods on GPU-Driven LLM Workloads
Dominik Scheinert, Alexander Acker, Thorsten Wittkopp +6
Large language model (LLM) services have become an integral part of search, assistance, and decision-making applications. However, unlike traditional web or microservices, the hard…
Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study
Philipp Wiesner, Soeren Becker, Brett Cornick +3
Training large language models (LLMs) requires substantial compute and energy. At the same time, renewable energy sources regularly produce more electricity than the grid can absor…
What happens when nanochat meets DiLoCo?
Alexander Acker, Soeren Becker, Sasho Nedelkoski +3
Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distribute…
Distributed Low-Communication Training with Decoupled Momentum Optimization
Sasho Nedelkoski, Alexander Acker, Odej Kao +2
The training of large models demands substantial computational resources, typically available only in data centers with high-bandwidth interconnects. However, reducing the reliance…
Sizey: Memory-Efficient Execution of Scientific Workflow Tasks
Jonathan Bader, Fabian Skalski, Fabian Lehmann +4
As the amount of available data continues to grow in fields as diverse as bioinformatics, physics, and remote sensing, the importance of scientific workflows in the design and impl…
Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling
Jonathan Will, Dominik Scheinert, Jan Bode +3
Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workl…
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…
Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications
Dominik Scheinert, Soeren Becker, Jonathan Will +2
Performance modeling can help to improve the resource efficiency of clusters and distributed dataflow applications, yet the available modeling data is often limited. Collaborative…
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…
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…
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…
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…