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  -