The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.
The 2018 William C. Carter PhD Dissertation Award in Dependability has been awarded to Christoph Borchert for his disseration "Aspect-Oriented Technology for Dependable Operating Systems" done at the Technische Universität Dortmund, Germany. Christoph will be presenting his dissertation at the 2018 International Conference on Dependable Systems and Networks (DSN) in Luxembourg in late June.
Abstract:
Modern computer devices exhibit transient hardware faults that disturb the electrical behavior but do not cause permanent physical damage to the devices. Transient faults are caused by a multitude of sources, such as fluctuation of the supply voltage, electromagnetic interference, and radiation from the natural environment. Therefore, dependable computer systems must incorporate methods of fault tolerance to cope with transient faults. Software-implemented fault tolerance represents a promising approach that does not need expensive hardware redundancy for reducing the probability of failure to an acceptable level.
This thesis focuses on software-implemented fault tolerance for operating systems because they are the most critical pieces of software in a computer system: All computer programs depend on the integrity of the operating system. However, the C/C++ source code of common operating systems tends to be already exceedingly complex, so that a manual extension by fault tolerance is no viable solution. Thus, this thesis proposes a generic solution based on Aspect-Oriented Programming (AOP).
To evaluate AOP as a means to improve the dependability of operating systems, this thesis presents the design and implementation of a library of aspect-oriented fault-tolerance mechanisms. These mechanisms constitute separate program modules that can be integrated automatically into common off-the-shelf operating systems using a compiler for the AOP language. Thus, the aspect-oriented approach facilitates improving the dependability of large-scale software systems without affecting the maintainability of the source code. The library allows choosing between several error-detection and error-correction schemes, and provides wait-free synchronization for handling asynchronous and multi-threaded operating-system code.
This thesis evaluates the aspect-oriented approach to fault tolerance on the basis of two off-the-shelf operating systems. Furthermore, the evaluation also considers one user-level program for protection, as the library of fault-tolerance mechanisms is highly generic and transparent and, thus, not limited to operating systems. Exhaustive fault-injection experiments show an excellent trade-off between runtime overhead and fault tolerance, which can be adjusted and optimized by fine-grained selective placement of the fault-tolerance mechanisms. Finally, this thesis provides evidence for the effectiveness of the approach in detecting and correcting radiation-induced hardware faults: High-energy particle radiation experiments confirm improvements in fault tolerance by almost 80 percent.
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We are very happy that Benjamin Sliwa from the Communication Networks Institute (CNI) has received the Best Student Paper award of the IEEE Vehicular Technology Conference (VTC) Spring-2018, which took place in June in Porto, Portugal (see photo). The VTC is the flagship conference of the Vehicular Technology Society within the IEEE and is typically attended by approx. 600 international scientists with a focus on wireless and mobile communications. The contribution "Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Network" has been co-authored by further CNI members Robert Falkenberg, Johannes Pillmann and Christian Wietfeld jointly with Thomas Liebig from the Computer Science department. It was selected from over 200 papers of the conference with PhD students as first author. The paper reports on key results of the research of projects B4 "Analysis and Communication for Dynamic Traffic Prognosis" and A4 "Resource efficient and distributed platforms for integrative data analysis" within the Collaborative Research Centre (SFB 876). The results of the paper demonstrate the significant potential of machine-learning for the optimization of mobile networks.
The paper can be found here:
and also within the coming weeks in the IEEE Xplore electronic proceedings.
Peter Marwedel gives the opening talk "Cyber-Physical Systems: Opportunities, Challenges, and (Some) Solutions" at the 21. workshop on Synthesis And System Integration of Mixed Information technologies (SASIMI) in Matsue, Japan. He presents opportunities and challenges of the design of cyber-physical systems. He will additionally attend the panel "What is the next place to go, in the era of IoT and AI?".
