Jonathan Decker
Biography
Jonathan is a scientific employee of the Georg-August-University of Göttingen and a PhD student of Julian Kunkel.
He takes the role of a system architect and is focused on designing systems that enable new and novel ways of utilizing Cloud and HPC resources, while also being efficient, secure and scalable. Most notably, he strives to combine HPC with Kubernetes.
ORCID: 0000-0002-7384-7304
Research Interests
- Kubernetes
- Serverless
Teaching
Autumn Term 2027
Summer Term 2027
Autumn Term 2026
Summer Term 2026
Autumn Term 2025
Summer Term 2025
Autumn Term 2024
Summer Term 2024
Autumn Term 2023
Summer Term 2023
Autumn Term 2022
Summer Term 2022
Open Thesis Topics
Performance Evaluation of LLM Inference EnginesApply
While vLLM is a widely spread inference backend engine for operating LLMs, there are alternative options that have the potential to deliver better performance by replacing or extending vLLM. Notable options are the Modular platform with MAX, ServerlessLLM and LMCache. Performance improvements may be limited to certain use cases. The overarching goal of this topic is to explore potential performance improvements for the Chat AI platform.
Operating Kubernetes with AI EngineersApply
Projects such as K8sGPT as well as MCP servers for Kubernetes enable LLMs to directly interact with Kubernetes clusters. This project aims to explore how well it is possible to maintain a given Kubernetes cluster with LLM-based engineers to complete typical maintenance tasks such as adjusting workloads and migrating between versions.
Prototyping a Geo-Redundancy EngineApply
As part of our goals for the SAIA platform, which operates Chat AI, we want it to operate with geo-redundancy such that even if a given geo-location experiences an outage, the service stays operational. To achieve this, a geo-redundancy engine should be prototyped, which can itself operate with multiple redundant instances and is able to synchronize service configurations across multiple geo-locations.
Theses
- The Potential of Serverless Kubernetes-Based FaaS Platforms for Scientific Computing Workloads, Jonathan Decker (Master's Thesis), Advisors: Dr. Julian Kunkel, 2022-01-31, BibTeX URL
Publications
2024
- A Quantitative and Qualitative Comparison of Machine Learning Inference Frameworks (Egi Brako, Jonathan Decker, Julian Kunkel), pp. 7 to 13, SCALABILITY 2024, Valencia, Spain, ISBN: 978-1-68558-216-6, 2024-11-17 BibTeX URL
- Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services (Ali Doosthosseini, Jonathan Decker, Hendrik Nolte, Julian Kunkel), 2024-08-02 BibTeX URL DOI
2023
- DECICE: Device-Edge-Cloud Intelligent Collaboration Framework (Julian Kunkel, Christian Boehme, Jonathan Decker, Fabrizio Magugliani, Dirk Pleiter, Bastian Koller, Karthee Sivalingam, Sabri Pllana, Alexander Nikolov, Mujdat Soyturk, Christian Racca, Andrea Bartolini), ACM, Computing Frontiers, ISBN: 979-8-4007-0140-5/23/05, 2023-05-09 BibTeX DOI PDF
2022
- Performance Evaluation of Open-Source Serverless Platforms for Kubernetes (Jonathan Decker, Piotr Kasprzak, Julian Kunkel), In Algorithms, MDPI, ISSN: 1999-4893, 2022-06-02 BibTeX URL DOI PDF
All publications as BibTex