TY  - JOUR
A1  - Grohmann, Johannes
A1  - Herbst, Nikolas
A1  - Chalbani, Avi
A1  - Arian, Yair
A1  - Peretz, Noam
A1  - Kounev, Samuel
T1  - A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
JF  - Computers
N2  - 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.
KW  - taxonomy
KW  - survey
KW  - failure prediction
KW  - anomaly prediction
KW  - anomaly detection
KW  - self-aware computing
KW  - self-adaptive systems
KW  - performance prediction
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200594
SN  - 2073-431X
VL  - 9
IS  - 1
ER  - 
TY  - JOUR
A1  - Kaiser, Dennis
A1  - Lesch, Veronika
A1  - Rothe, Julian
A1  - Strohmeier, Michael
A1  - Spieß, Florian
A1  - Krupitzer, Christian
A1  - Montenegro, Sergio
A1  - Kounev, Samuel
T1  - Towards Self-Aware Multirotor Formations
JF  - Computers
N2  - 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.
KW  - self-aware computing
KW  - unmanned aerial vehicles
KW  - multirotors
KW  - quadcopters
KW  - intelligent transportation systems
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200572
SN  - 2073-431X
VL  - 9
IS  - 1
ER  -