@article{KaiserLeschRotheetal.2020,
  author    = {Kaiser, Dennis and Lesch, Veronika and Rothe, Julian and Strohmeier, Michael and Spieß, Florian and Krupitzer, Christian and Montenegro, Sergio and Kounev, Samuel},
  title     = {Towards Self-Aware Multirotor Formations},
  series = {Computers},
  volume    = {9},
  journal   = {Computers},
  number    = {1},
  issn      = {2073-431X},
  doi       = {10.3390/computers9010007},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200572},
  pages     = {7},
  year      = {2020},
  abstract  = {In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.},
  language  = {en}
}
@article{GrohmannHerbstChalbanietal.2020,
  author    = {Grohmann, Johannes and Herbst, Nikolas and Chalbani, Avi and Arian, Yair and Peretz, Noam and Kounev, Samuel},
  title     = {A Taxonomy of Techniques for SLO Failure Prediction in Software Systems},
  series = {Computers},
  volume    = {9},
  journal   = {Computers},
  number    = {1},
  issn      = {2073-431X},
  doi       = {10.3390/computers9010010},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200594},
  pages     = {10},
  year      = {2020},
  abstract  = {Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.},
  language  = {en}
}