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    Item type:Publication, Access status: without files ,
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    From Global to Local Quiescence: Wait-Free Code Patching of Multi-Threaded Processes
    (2020)
    Rommel, Florian
    ;
    Dietrich, Christian
    ;
    Friesel, Daniel
    ;
    Köppen, Marcel
    ;
    Borchert, Christoph
    Live patching has become a common technique to keep long-running system services secure and up-to-date without causing downtimes during patch application. However, to safely apply a patch, existing live-update methods require the entire process to enter a state of quiescence, which can be highly disruptive for multi-threaded programs: Having to halt all threads (e.g., at a global barrier) for patching not only hampers quality of service, but can also be tremendously difficult to implement correctly without causing deadlocks or other synchronization issues. In this paper, we present WfPatch, a wait-free approach to inject code changes into running multi-threaded programs. Instead of having to stop the world before applying a patch, WfPatch can gradually apply it to each thread individually at a local point of quiescence, while all other threads can make uninterrupted progress. We have implemented WfPatch as a kernel service and user-space library for Linux 5.1 and evaluated it with OpenLDAP, Apache, Memcached, Samba, Node.js, and MariaDB on Debian 10 (“buster”). In total, we successfully applied 33 different binary patches into running programs while they were actively servicing requests; 15 patches had a CVE number or were other critical updates. Applying a patch with WfPatch did not lead to any noticeable increase in request latencies — even under high load — while applying the same patch after reaching global quiescence increases tail latencies by a factor of up to 41x for MariaDB.
    Publicationtype: Conference Paper
      47
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    Challenges of order reduction techniques for problems involving polymorphic uncertainty
    (2019-05)
    Pivovarov, Dmytro
    ;
    Willner, Kai
    ;
    Steinmann, Paul
    ;
    Brumme, Stephan
    ;
    Müller, Michael
    Modeling of mechanical systems with uncertainties is extremely challenging and requires a careful analysis of a huge amount of data. Both, probabilistic modeling and nonprobabilistic modeling require either an extremely large ensemble of samples or the introduction of additional dimensions to the problem, thus, resulting also in an enormous computational cost growth. No matter whether the Monte-Carlo sampling or Smolyak's sparse grids are used, which may theoretically overcome the curse of dimensionality, the system evaluation must be performed at least hundreds of times. This becomes possible only by using reduced order modeling and surrogate modeling. Moreover, special approximation techniques are needed to analyze the input data and to produce a parametric model of the system's uncertainties. In this paper, we describe the main challenges of approximation of uncertain data, order reduction, and surrogate modeling specifically for problems involving polymorphic uncertainty. Thereby some examples are presented to illustrate the challenges and solution methods.
    Publicationtype: Journal Article
      371
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