The Operating Systems and Middleware group, led by Prof. Dr. Andreas Polze, focuses on programming paradigms, design patterns and description techniques for large, distributed component systems. In particular, the connection of middleware and embedded systems and their predictable behavior in terms of real-time capability, fault tolerance, and security are the topics here. Prof. Polze is also spokesperson of the HPI Research School, HPI's international research college, and member of the steering committee of HPI’s Future SOC Lab.

recent posts

current teaching activities

• Betriebssysteme I
• Verantwortung der Informatik – Accountability In AI
• Eingebettete Betriebssysteme
• Research Seminar 2020/21
• Bachelorprojekt 2021/2022: "WiseMat"

current projects

Free of charge access for researchers to a powerful IT infrastructure.

Smart Level Crossings (RealLabHH TP 8, National Platform Future of Mobility)

Transfer of distributed ledger technologies to the railway sector.

Developing a telemedicine system for the support of cardiological patients.

current open source projects

Near-storage compute-aware file system and FPGA operator pipelines.

Co-simulation and Hybrid Testbed for Distributed Applications.

A C++ parallel programming framework for NUMA systems, based on PGAS semantics.

For further projects, please refer to the OSM group on GitHub and the OSM group on GitLab.

current topics

• resource management from core to Cloud
  • dynamic workload management in distributed systems
  • dynamic, workload-dependent resource scaling (e.g., dynamic LPARs)
  • memory optimizations for virtual machines (e.g., memory migration benchmarks)
  • NUMA-aware programming with the PGASUS framework: applications, case studies, benchmarks
  • architectures of future server systems
  • lock-free data structures
  • multicore/NUMA
  • advanced topology discovery with bandwith and latency measurements: find shared interconnects
• accelerator programming / heterogeneous computing
  • high-Level programming facilities for distributed GPU computing (e.g., CloudCL)
  • high-level programming facilities for FPGAs (e.g., CAPI SNAP, OpenCL)
  • virtualization / Containerization for GPUs or FPGAs
  • FPGAs in IaaS Cloud Resources (e.g., Amazon F instances)
  • hardware-accelerated memory-compression (e.g., DEFLATE, 842 compression)
  • evaluation of integrated GPUs and APUs for latency-critical workloads (e.g., audio processing)
  • new programming languages & frameworks: CAPI SNAP, Radeon Open Compute, etc.
• dependability and fault injection
  • assessment
  • fault tolerance of service-oriented architectures
  • fault injection in distributed systems
  • microservice architectures
  • decentralized architectures (e.g., distributed ledgers)
  • Internet of Things (e.g., Rail2X, IEEE 802.11p)