collaborators

5 papers

cs.DC2026

BlobShuffle: Cost-Effective Repartitioning in Stream Processing Systems via Object Storage Exemplified with Kafka Streams

Sören Henning, Otmar Ertl, Adriano Vogel

Shuffling or repartitioning data streams is an essential operation of state-of-the-art stream processing frameworks to support stateful workloads in a large-scale, distributed sett…

cs.DB2026

FluxSieve: Unifying Streaming and Analytical Data Planes for Scalable Cloud Observability

Adriano Vogel, Sören Henning, Otmar Ertl

Despite many advances in query optimization, indexing techniques, and data storage, modern data platforms still face difficulties in delivering robust query performance under high…

cs.SE2025

Should I Run My Cloud Benchmark on Black Friday?

Sören Henning, Adriano Vogel, Esteban Perez-Wohlfeil +2

Benchmarks and performance experiments are frequently conducted in cloud environments. However, their results are often treated with caution, as the presumed high variability of pe…

cs.SE2025

When Should I Run My Application Benchmark?: Studying Cloud Performance Variability for the Case of Stream Processing Applications

Sören Henning, Adriano Vogel, Esteban Perez-Wohlfeil +2

Performance benchmarking is a common practice in software engineering, particularly when building large-scale, distributed, and data-intensive systems. While cloud environments off…

cs.SE2025

Analyzing Logs of Large-Scale Software Systems using Time Curves Visualization

Dmytro Borysenkov, Adriano Vogel, Sören Henning +1

Logs are crucial for analyzing large-scale software systems, offering insights into system health, performance, security threats, potential bugs, etc. However, their chaotic nature…