Jonathan Decker

Jonathan is a scientific employee under Julian Kunkel at the Georg-August-University of Göttingen, Germany.
He completed his master's degree on Serverless computing using Kubernetes in 2022 and is now looking to pursue a PhD.
ORCID: 0000-0002-7384-7304

  • Kubernetes
  • Serverless

Containers for Parallel ApplicationsApply

Parallel applications on HPC systems often rely on system specific MPI (Message Passing Interface) and interconnect libraries, for example for Infiniband or OmniPath networks. This partially offsets one main advantage of containerizing such applications, namely the portability between different platforms. The goal of this project is to evaluate different ways of integrating system specific communication libraries into containers, allowing for porting these containers to a different platform with minimal effort. A PoC should be implemented and benchmarked against running natively on a system.

Fixing Shortcomings of Kubernetes Severless TechnologiesApply

Serverless Computing or Function-as-a-Service (FaaS) has emerged as a new paradigm for computing over the last few years. There exists a number of open source FaaS platforms based on Kubernetes as the container orchestration platform maps well to the components required for FaaS. However, most approaches to FaaS are still relatively naive and leave many performance improvements on the table. This work focuses on said limitations and aims to solve at least one of them and implement a proof of concept. Finally, the performance improvements should be benchmarked in a virtualized environment and on the HPC system.

Evaluating the Capabilities of K8SGPTApply

K8SGPT (https://k8sgpt.ai/) is a Kubernetes tool that can use the OpenAI API or self-hosted AI APIs (such as LocalAI https://github.com/go-skynet/LocalAI) to analyse a given cluster. On paper this sounds great as it allows finding and fixing the relevant information within the complexity of a K8s cluster. But how capable is it really? What limitations apply, what is the overhead and how do the OpenAI API and LocalAI compare? For this topic, methods for the evaluation should be developed and applied to test clusters. Finally, a recommendation should be given on which use cases can benifit from K8SGPT and which not.

Confidential GPU InferenceApply

For customer facing systems that handle sensitve data such as patient information, it is required to comply with strict data protection laws. In order to comply with these laws even during a security breach, confidential computing should be used, however, modern use-cases require the usage of scable multi-user systems with GPU acceleration for ML inference workloads. This thesis encapsulates setting up confidential computing on top of a Kubernetes cluster using Kata Containers, Confidential Containers and Nvidia Confidential GPU Computing as well as measuring the performance costs of using a confidential compute stack.

  • 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
  • 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

All publications as BibTex

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  • Last modified: 2023-08-28 10:40
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