Professorship for Operating Systems and Middleware
At the group Operating Systems and Middleware group led by Prof. Dr. Andreas Polze, work is done "close to the metal".
Firstly, new hardware trends that are to be utilized. This involves programming paradigms, design patterns and
description techniques for large, distributed component systems and data centers. Part of the focus is how to measure
and control energy consumption there as well.
The other metal involves rails: Several projects are exploring new architectures for flexible, distributed rail
control systems to control signals and switches. We examine real-time capability, fault tolerance and reliability
through simulated experiments, in our laboratory, and directly on the track.
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.
current teaching activities
current open source projects
- 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
- 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.
- assessment and benchmarking
- operation of heterogeneous infrastructures
- fault tolerance of service-oriented architectures
- microservice architectures
- software fault injection (network, OS, communication, etc.)
- automation (lab, operation, assessment, etc.)
- error detection – coverage vs. complexity
- decentralized architectures (e.g., distributed ledgers)
- Internet of Things (e.g., Rail2X, IEEE 802.11p)