A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
Please always quote using this URN: urn:nbn:de:bvb:20-opus-200594
- 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),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.…
Author: | Johannes Grohmann, Nikolas Herbst, Avi Chalbani, Yair Arian, Noam Peretz, Samuel Kounev |
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URN: | urn:nbn:de:bvb:20-opus-200594 |
Document Type: | Journal article |
Faculties: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Language: | English |
Parent Title (English): | Computers |
ISSN: | 2073-431X |
Year of Completion: | 2020 |
Volume: | 9 |
Issue: | 1 |
Pagenumber: | 10 |
Source: | Computers 2020, 9(1), 10; https://doi.org/10.3390/computers9010010 |
DOI: | https://doi.org/10.3390/computers9010010 |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Tag: | anomaly detection; anomaly prediction; failure prediction; performance prediction; self-adaptive systems; self-aware computing; survey; taxonomy |
Release Date: | 2020/08/31 |
Date of first Publication: | 2020/02/11 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |