Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI
  
Login
  • de
 

Current Projects

The current period is Spring 2020 and started April 21, 2020. The end is the next HPI Future SOC Lab Day on November 03, 2020. 

17 research projects are using the Lab's IT infrastructure. If you would like to receive more information about one or more projects, please contact us.

Germany

Cloud Computing Resource Management using Machine Learning

Abstract

In this project, we use machine learning techniques to predict the optimal timing for running a live migration request. This optimal timing approach is based on using machine learning for live migration cost prediction and datacenter network utilization prediction

 

Researchers

Principle Investigator: Prof. Dr. Christoph Meinel || Contact Author: Mohamed Elsaid || Hasso-Plattner-Institute 

Machine Learning to scale telemedical interventions for cardiovascular diseases

Abstract

Cardiovascular diseases are the leading cause of death globally. Telemedicine interventions were shown to reduce the percentage of days lost due to unplanned cardiovascular hospital admissions and all-cause mortality. This project will train an AI-System that could help to scale such telemedical interventions by preprocessing and prioritizing.

 

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Jossekin Beilharz || Hasso Plattner Institute

Measurement-Based Software Performance Engineering for Microservices and Multi-Core Systems

Abstract

DevOps, microservices, and multi-core systems are gaining considerable attraction in research and practice. We would use the requested HPI Future SOC Lab resources to investigate the experimental evaluation of our activities on DevOps-oriented "Load Testing for Microservices" and "Software Performance Engineering for Multi-Core Systems".

 

Researchers

Principle Investigator: Dr.-Ing. Andre van Hoorn || Contact Author: Markus Frank || University of Stuttgart

Behaviour-based authentication: feature engineering based on large user profiles

Abstract

Our project contributes to the field of behaviour-based authentication. In the last years we collected a great amount of walking sequences of several people. As this dataset is too large to be processed on a normal machine, we hope to evaluate and improve our authentication model by the support of additional resources.

 

Researchers

Principle Investigator: Prof. Dr. Christoph Meinel || Contact Author: Vera Weidmann || neXenio GmbH

Train and optimize a German wav2letter++ Speechprocessing model for medical terms

Abstract

Medical documentation is a huge burden across all healthcare professions. In many cases speech recognition could provide great benefits but even modern speech processing models struggle with medical terms. By creating a medical data set and training a German model with the new wav2letter++ speech processing toolkit, we aim to improve that.

 

Researchers

Principle Investigator: Prof. Dr. Christoph Lippert || Contact Author: Marcel Schmidberger || Hasso Plattner Institute

Exploring Game-Theoretic Formation of Realistic Networks

Abstract

We have developed an agent-based game-theoretic model which promises a good explanation of the structure of real world networks. Previous large-scale experiments revealed that our model fails to produce networks with non-constant diameter. This project aims at simulating variations of the model that allow a more flexible diameter

 

Researchers

Principle Investigator: Prof. Dr. Tobias Friedrich || Contact Author: Dr. Pascal Lenzner || Hasso Plattner Institute

Integrating Hardware Accelerators in Virtualized Environments

Abstract

In this project, we study mechanisms for integrating hardware accelerators in virtual machines and cloud infrastructures. Exemplary workloads include In-Memory Databases, scientific computation and multimedia applications. This project is a continuation of preceding projects conducted in the Spring and Fall Periods of 2018.

 

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Max Plauth || Hasso Plattner Institute

An Energy-Aware Runtime System for Heterogeneous Clusters

Abstract

We are planning to evaluate our Albatross runtime system (DOI: 10.1145/3217189.3217193) for energy-efficient and economic processing on heterogeneous compute infrastructures in the HPI Future SOC Lab in cooperation with the OSM Group (HPI).

 

Researchers

Principle Investigator: Prof. Dr.-Ing. habil Wolfgang Schröder-Preikschat || Contact Author: Dr.-Ing. Timo Hönig || Friedrich-Alexander-Universität Erlangen-Nürnberg

Folding@Future SOC Lab

Abstract

Folding@home is a project focused on disease research. Solving those problems require many computer calcul­ations. Folding@home COVID-19 projects started with a high GPU demand and now extended to CPU resources also. The HPI Future SOC Lab tries to provide as much available resources as possible to support the fight against COVID-19 and health.

 

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Bernhard Rabe || Hasso Plattner Institute

PRESLEY - Page Replication for Scale-Up Systems

Abstract

We want to evaluate how page replication as another management mechanism is worth the effort. The experiments are based on a patched linux kernel running on multi-socket NUMA systems. The outcome should be to identify possible workloads and data structures that likely benefit from the replication of pages.

 

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Felix Eberhardt || Hasso Plattner Institute

Analysis of the chronological usage of JavaScript libraries across the WWW

Abstract

The decision for the best JavaScript library to use in web applications is difficult, due to the vast number and the short lifespan of a lot of them. This project strives to apply the decisions made in the past to the future, by analysing a dataset of web crawls, provided by Common Crawl, what may causes libraries to crumble and others to rise.

 

Researchers

Principle Investigator: Sven Buchholz || Contact Author: Robert Beilich || Technische Hochschule Brandenburg

 

Information Retrieval for Cultural Heritage Data

Abstract

The Wildenstein Plattner Institute is undertaking a massive digitization project, with the goal to make millions of previously unpublished cultural heritage information available to the broader public. Because of the mass of information, it can only be processed using ML techniques like advanced OCR and image processing algorithms

 

Researchers

Principle Investigator: Prof. Dr. Christoph Meinel || Contact Author: Christian Bartz || Wildenstein Plattner Institute

Europe

WEEVIL- Fifth (WEEVIL5)

Abstract

The WEEVIL5 project is designed as the extension of the previous one (WEEVILF), which was developed using the RX600S5-1 server from the HPI Future SOC Lab. This document details the main aims, scope and schedule development for the WEEVIL5 project. This project is a continuation of previous one.

 

Researchers

Principle Investigator: Prof. Dr. Carlos Juiz || Contact Author: Belen Bermejo || Universitat de les Illes Balears

Benchmarking Java on Ethernet Cluster

Abstract

The aim of this project is to check the scalability of parallel, network intensive microbenchmarks and application written in Java, using the PCJ library, HPC Challenge 2014 award-winning Java library for high-performance parallel computing, on the 1000 Core Cluster - with high performance Ethernet interfaces.

 

Researchers

Principle Investigator: Dr. Marek Nowicki || Contact Author: Dr. Marek Nowicki || Nicolaus Copernicus University in Toruń

Fast and Non Invasive Diagnosis of SARS-COV-2 Infections via Raman Spectroscopy and Deep Learning

Abstract

Recent researches show the possibility of decoding spectral signals obtained through Raman spectroscopy via deep learning techniques. Nowadays, during the COVID emergency, the application of the same techniques could allow for a fast and non-invasive diagnosis of SARS-COV-2 infection based on Raman spectra from human salivary samples.

 

Researchers

Principle Investigator: Vincenzina Messina || Contact Author: Dario Bertazioli || Università degli Studi di Milano-Bicocca

Worldwide

CitySensing

Abstract

Big mobility and IoT data processing and analytics in Smart Cities
The planned activities are focused on research and development of methods, tools and software systems for efficient processing, analysis, mining and visualization of Big mobility data collected using mobile crowd sensing and Internet of Things paradigms in Smart Cities.

 

Researchers

Principle Investigator: Prof. Dragan Stojanovic || Contact Author: Prof. Dragan Stojanovic || University of Nis, Serbia

Designing practical algorithms through overfitting

Abstract

Since 2017, the teams around Prof. Tobias Friedrich (HPI, Chair for Algorithm Engineering) and Dr Markus Wagner (University of Adelaide, Australia) have explored the concept of automated algorithm configuration to design new search operators. This project builds upon the existing work and will push it toward real-world, interconnected problems.

 

Researchers

Principle Investigator: Dr. Markus Wagner || Contact Author: Dr. Markus Wagner || University of Adelaide, Australia