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Research Interests

The operating systems group pursues research on scalable virtualization technologies. To this end, we focus on efficient main memory deduplication techniques and policies. We moreover develop a distributed environment for system simulation that enables practicable research on the effects that novel memory architectures have on existing operating systems and workloads. Another focus lies in the design of efficient microkernels in multi-server systems and on the distributed execution of such systems in clusters. The operating systems group further investigates the use of knowledge on the inner workings of hardware components to estimate and control their energy consumption at runtime. This allows determining, optimizing, and throttling the energy consumption per task/thread. Target systems range from small battery-powered sensor nodes to thermally constrained multi-processor and multi-core systems.


Power Management (Contact: Prof. Dr. Frank Bellosa)

With the emergence of portable and wireless devices and with the thermal problems originating from high-power processors we are facing a rising awareness for the topic of dynamic energy management. The interface between the hardware, whose energy consumption should be controlled, and the application software, which consumes energy implicitly by activating hardware components, is the operating system. Because of the operating system's role as a mediator, it is predestined for any kind of resource accounting. This includes the aspect of energy as an indispensable first class resource.


Memory Deduplication (Contact: Dr. Konrad Miller)

Limited memory sizes and latencies have become a primary bottleneck. In cloud-computing scenarios, the number of virtual systems that can be colocated on a physical system mainly depends on the amount of available memory on the hosting server. In this project, we research novel methods for main memory deduplication as well as techniques that make memory deduplication more efficient.


Full System Simulation and Memory Tracing
(Contact: Dipl.-Inform. Marc Rittinghaus, Dipl.-Inform. Thorsten Groeninger)

With full system simulation an entire physical machine can be simulated on top of a host operating system (OS). Simulation thus provides a powerful foundation to study the runtime behavior and interaction of computer architecture, operating systems and applications. However, current solutions for full system simulation suffer from high slowdowns and do not encompass support for detailed tracing of operations carried out in the simulated machine (e.g., memory accesses). That limits the applicability of simulation-based performance analysis and debugging as well as complicates extracting desired information. In the Simutrace project, we aim at establishing technologies to overcome current limitations. To that end, we are researching new ways to accelerate full system simulation and to ease the analysis of workloads through tracing.


L4Ka Project (Contact: Dr. Jan Stoess)

Our primary research focus aims at substantiating and establishing a new methodology for system construction that helps to manage ever-increasing OS complexity and minimizes legacy dependence. Our vision is a microkernel technology that can be and is used advantageously for constructing any general or customized operating system including pervasive systems, deep-computing systems, and huge servers.


L4Ka Virtualization (Contact: Prof. Dr. Frank Bellosa)

Our Virtual Machine project investigates the applicability of microkernel technology to virtual machine environments. It complements the L4Ka project, using the L4Ka microkernel for high performance reuse of legacy software with the added benefits of strong isolation. Traditional virtual machine environments achieve their security via extreme isolation of compartments; the L4Ka Virtual Machine project explores the benefits of enabling secure communication between compartments, to construct large systems from legacy components. The project also focuses on achieving new levels of performance and scalability in virtual machines.