The last years have seen a dramatic change in the hardware landscape. The advent of multi- and manycore processors has come with the need to change also the programming models. Software developers need to exploit parallelism in order to achieve good performance from the CPU. Beside the increase in the number of cores the heterogeneity has increase significantly. Beside the compute cores also other elements of the computer systems have increased in complexity and hierarchy such as Cache, Memory access and interconnect networks.

Similar challenges have to be addressed in modern Cloud applications running on multiple parallel instances, potentially across different cloud operators. Beside parallelism also increased heterogeneity in resource types and the demand for data locality demands for new approaches and concept to dynamically map the application on the available (virtualized) resources.

At the institute we perform research with partners from research and industry in the frame of national and European projects in order to find solutions for these challenges. Further information can be found on our project page.


Current areas of research

The institute is in the process of building up a test-cluster system built with different hardware elements from energy efficient CPU over accelerators up to manycore systems to evaluate different approaches and methods for optimization of cloud image placement and programming models.

Research Areas

  • Knowledge Representation & Stream Reasoning in Smart Interconnected Environments

    • Meaningful abstractions for streaming data while evaluating different pattern extraction techniques
    • Temporal representation of events and their effects in streaming environments
    • Dealing with uncertainty and incompleteness in Stream Reasoning using probabilistic graphical models
    • Hypothetical extrapolation of knowledge, to model unobservables or alternatives for sensing,  via non-monotonic/abductive reasoning
    • A logic-based abductive framework for indirect sensing is being developed as a first result of the work, currently named ATOPO.








  • Autonomous Infrastructure Management

    • Collecttion of Data and Metris for Cluster Management based on the co-developed TIMACS framework
      (more information)








  • Energy efficient Computing

    • Context-Aware Topology Optimisation and Virtual Machine Placement for Cloud Environment
    • Energy efficient Compute System architectures
      • Energy efficient component integration e.g. low power CPUs
      • Integration with the facility environment (heat re-use)








  • Cloud Computing

    • Cloud executionware dealing with the platform specific mapping of the application to the architectural model and Application Programming Interfaces (APIs) of the execution infrastructure of the Cloud provider, and with capabilities of monitoring the running application and possible reconfiguration to optimise its behavior in particular within the EU project PaaSage (see
    • Future Cloud Architectures








  • Heterogeneous Computing Systems

    • Programming models for heterogeneous systems for embedded and high performance computing incorporating notions of cost for communication, data usage and access, algorithmic description
    • Operating Systems for large scale heterogeneous infrastructures that create minimal overhead for the system and thus exploits the resources best
    • Real-Time Systems & Scheduling with adaptive resource reservations and quality management for dynamic environments with unpredictable and fluctuating computational loads








  • Automated Evaluations of Distributed DBMS on Elastic Infrastructures

    • modern NoSQL and NewSQL Database Management Systems (DBMS) promise to deliver the non-functional features high-performance, scalability, elasticity and availability
    • elastic infrastructures such as the cloud  have become the preferred option to operate distributed DBMS as the cloud provides scalability and elasticity on the resource level
    • significant evaluations of the non-functional DBMS features requires the consideration of multiple DBMS- and Cloud-specific properties, demanding for supportive DBMS evaluation frameworks that automate the evaluation process and ensure reproducible evaluations
    • first research results have led to the Mowgli framework that enables the automated DBMS evaluations for the evaluation objectives performance, scalability, elasticity and availability    








Victorgrigas CC BY-SA 3.0